Image processing device and method, recording medium, and program

ABSTRACT

An image processing device includes a viewpoint separating unit configured to separate multi-viewpoint image data, including images of multiple viewpoints and representing intensity distribution of light and the direction of travel of light according to positions and pixel values of pixels, into a plurality of single-viewpoint image data for each of the individual viewpoints; and a parallax control unit configured to control amount of parallax between the plurality of single-viewpoint image data obtained by separation into individual viewpoints by the viewpoint separating unit.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 13/674,333, filed Nov. 12, 2012, which claims thebenefit of priority from prior Japanese Priority Patent Application JP2011-258518 filed in the Japan Patent Office on Nov. 28, 2011, theentire content of which is hereby incorporated by reference.

BACKGROUND

The present disclosure relates to an image processing device and method,a recording medium, and a program, and particularly relates to an imageprocessing device and method, a recording medium, and a program, wherebystereoscopic effect of multi-viewpoint images can be controlled.

Heretofore, various imaging apparatuses have been proposed anddeveloped. Also, there have been proposed imaging apparatuses whichperform predetermined image processing on imaged data obtained byimaging, following which the imaged data is output.

For example, there has been proposed an imaging apparatus employing whatis called “light field photography”, referred to as a “light fieldcamera” (e.g., see Japanese Unexamined Patent Application PublicationNo. 2009-165115). This imaging apparatus includes an imaging lens, amicrolens array, an imaging device, and an image processing unit, withan aperture diaphragm having a single aperture being provided to themiddle portion of the imaging lens. Due to this configuration, imageddata acquired by the imaging device includes, in addition to intensitydistribution of light at the light receiving face, information of thedirection of travel of light as well. The configuration also allows anobservation image at any field of view or focal point to bereconstructed at the image processing unit.

Multiple microlenses are provided to the microlens array, with multiplepixels of the imaging device being assigned to each microlens.Accordingly, by collecting pixel values of pixels each in the sameposition as to each microlens, for example, image data obtained byperforming photoelectric conversion of incident light each from the samedirection (i.e., an image from one direction) can be obtained. In thesame way, by managing the pixel values in accordance with the positionsthereof, multiple images of viewpoints which differ from each other(i.e., a multi-viewpoint image) can be obtained.

SUMMARY

However, in this case, the amount of parallax of the multi-viewpointimage is dependent on the aperture of the diaphragm of the imaging lens,and the number of pixels of the imaging device assigned to themicrolenses. Accordingly, with multi-viewpoint images obtained usingsuch an imaging apparatus, restriction in size of the imaging lens andsuppression in deterioration of resolution has led to insufficientamount of parallax, so sufficiently great visual stereoscopic effect(sense of depth) has been difficult.

It has been found desirable to enable control of stereoscopic effect ofmulti-viewpoint images, so as to obtain stereoscopic effect of a moresuitable level.

According to an embodiment of the present disclosure, an imageprocessing device includes: a viewpoint separating unit configured toseparate multi-viewpoint image data, including images of multipleviewpoints and representing intensity distribution of light and thedirection of travel of light according to positions and pixel values ofpixels, into a plurality of single-viewpoint image data for each of theindividual viewpoints; and a parallax control unit configured to controlamount of parallax between the plurality of single-viewpoint image dataobtained by separation into individual viewpoints by the viewpointseparating unit.

The parallax control unit may control the amount of parallax by addingor subtracting a derivative signal, obtained by performing derivation ofthe single-viewpoint image data, to or from each single-viewpoint imagedata.

The parallax control unit may detect disparity, which indicates amountof parallax of an object to be controlled, and, based on the detecteddisparity, correct the derivative signal and controls the amount ofparallax by adding or subtracting the derivative signal followingcorrection to or from each single-viewpoint image data.

The image processing device may further include: a super-resolutionprocessing unit configured to perform super-resolution processing whereimage resolution is raised to high resolution for each of the pluralityof single-viewpoint image data obtained by separation into individualviewpoints by the viewpoint separating unit; with the parallax controlunit controlling amount of parallax between the plurality ofsingle-viewpoint image data of which the resolution has been raised tohigh resolution by the super-resolution processing unit.

The super-resolution processing unit may detect disparity whichindicates amount of parallax, between each viewpoint, and performsuper-resolution processing of the single-viewpoint image data, usingthe detected disparity.

The parallax control unit may control the amount of parallax using thedisparity detected by the super-resolution processing unit.

The image processing device may further include: a noise reductionprocessing unit configured to perform noise reduction processing toreduce noise on each of the plurality of single-viewpoint image data, ofwhich the resolution has been raised to high resolution by thesuper-resolution processing unit; with the parallax control unitcontrolling the amount of parallax between the plurality ofsingle-viewpoint image data, of which noise has been reduced by thenoise reduction processing unit.

The noise reduction processing unit may perform motion detection withimages before and after processing, perform motion compensation on animage after processing using the detected motion vector, and calculatethe arithmetic mean of the image following processing that has beensubjected to motion compensation and the image before processing.

The noise reduction processing unit may use the detected motion vectorto perform the noise reduction processing on each of the plurality ofsingle-viewpoint image data.

The image processing device may further include: a super-resolution andnoise reduction processing unit configured to perform, on each of theplurality of single-viewpoint image data obtained by separation intoindividual viewpoints by the viewpoint separating unit, super-resolutionprocessing to raise the resolution of images to high resolution, andnoise reduction processing to reduce noise; with the parallax controlunit controlling the amount of parallax between the plurality ofsingle-viewpoint image data, of which resolution has been raised to highresolution and noise has been reduced by the super-resolution and noisereduction processing unit.

The super-resolution and noise reduction processing unit may perform thenoise reduction processing on each of the super-resolution processingresults of the multiple single-viewpoint image data, and perform thesuper-resolution processing using the noise reduction processingresults.

The image processing device may further include: an initial imagegenerating unit configured to generate an initial image using an imageof a frame of interest which is to be processed, and an image of a pastframe processed prior to the frame of interest; with thesuper-resolution and noise reduction processing unit performing thesuper-resolution processing using the initial image generated by theinitial image generating unit.

The initial image generating unit may detect motion between the image ofthe frame of interest and the image of the past frame, perform motioncompensation of the image of the past frame using the detected motionvector, and generate the initial image using the image of the past framesubjected to motion compensation and the image of the frame of interest.

The initial image generating unit may generate the initial image usingan image of a viewpoint of interest to be processed in the frame ofinterest, an image of the viewpoint of interest in the past frame, andan image of another viewpoint which is not the viewpoint of interest inthe past frame.

The image processing device may further include: a super-resolutionprocessing unit configured to perform super-resolution processing of thesingle-viewpoint image data to raise the resolution of an image to highresolution; a noise reduction processing unit configured to performnoise reduction processing to reduce noise of the single-viewpoint imagedata of which the resolution has been raised to high resolution by thesuper-resolution processing unit; and an image generating unitconfigured to generate the plurality of single-viewpoint image data,using the single-viewpoint image data of which the noise has beenreduced by the noise reduction processing unit; with the parallaxcontrol unit controlling the amount of parallax between the plurality ofsingle-viewpoint image data generated by the image generating unit.

The image processing device may further include: an initial imagegenerating unit configured to generate an initial image, using an imageof a frame of interest to be processed, and an image of a past frameprocessed prior to the frame of interest; with the super-resolutionprocessing unit performing the super-resolution processing using theinitial image generated by the initial image generating unit.

The image processing device may further include: a first storage unitconfigured to store the plurality of single-viewpoint image dataobtained by separation into individual viewpoints by the viewpointseparating unit; a viewpoint sequential readout unit configured to readout the plurality of single-viewpoint image data stored in the firststorage unit, one viewpoint at a time; a super-resolution processingunit configured to perform super-resolution processing to raise theresolution of the single-viewpoint image data read out from theviewpoint sequential readout unit to high resolution; a noise reductionprocessing unit configured to perform noise reduction processing toreduce the noise of the single-viewpoint image data of which theresolution has been raised to high resolution by the super-resolutionprocessing unit; and a second storage unit configured to store thesingle-viewpoint image data of which noise has been reduced by the noisereduction processing unit; with the parallax control unit controllingthe amount of parallax between the plurality of single-viewpoint imagedata stored in the second storage unit.

According to an embodiment of the present disclosure, an imageprocessing method of an image processing device includes: a viewpointseparating unit separating multi-viewpoint image data, including imagesof multiple viewpoints and representing intensity distribution of lightand the direction of travel of light according to positions and pixelvalues of pixels, into a plurality of single-viewpoint image data foreach of the individual viewpoints; and a parallax control unitcontrolling amount of parallax between the plurality of single-viewpointimage data obtained by separation into individual viewpoints.

According to an embodiment of the present disclosure, acomputer-readable recording medium in which is recorded a program causesa computer to function as: a viewpoint separating unit configured toseparate multi-viewpoint image data, including images of multipleviewpoints and representing intensity distribution of light and thedirection of travel of light according to positions and pixel values ofpixels, into a plurality of single-viewpoint image data for each of theindividual viewpoints; and a parallax control unit configured to controlamount of parallax between the plurality of single-viewpoint image dataobtained by separation into individual viewpoints by the viewpointseparating unit.

According to an embodiment of the present disclosure, a program causes acomputer to function as: a viewpoint separating unit configured toseparate multi-viewpoint image data, including images of multipleviewpoints and representing intensity distribution of light and thedirection of travel of light according to positions and pixel values ofpixels, into a plurality of single-viewpoint image data for each of theindividual viewpoints; and a parallax control unit configured to controlamount of parallax between the plurality of single-viewpoint image dataobtained by separation into individual viewpoints by the viewpointseparating unit.

According to an embodiment of the present disclosure, an multi-viewpointimage data, including images of multiple viewpoints and representingintensity distribution of light and the direction of travel of lightaccording to positions and pixel values of pixels, is separated into aplurality of single-viewpoint image data for each of the individualviewpoints, and amount of parallax between the plurality ofsingle-viewpoint image data obtained by separation into individualviewpoints is controlled.

According to the present disclosure, an image can be processed. Inparticular, stereoscopic effect of a multi-viewpoint image can becontrolled.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram for describing a configuration example of a lightfield camera;

FIG. 2 is a diagram for describing an example of the way in which amulti-viewpoint image is generated;

FIG. 3 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 4 is a block diagram illustrating a primary configuration exampleof an SR (super-resolution) unit;

FIG. 5 is a block diagram illustrating a primary configuration exampleof a super-resolution processing unit;

FIG. 6 is a block diagram illustrating a primary configuration exampleof a super-resolution processing unit;

FIG. 7 is a block diagram illustrating a primary configuration exampleof a high-frequency estimation unit;

FIG. 8 is a block diagram illustrating a primary configuration exampleof an image quality control unit;

FIG. 9 is a block diagram illustrating a primary configuration exampleof an NR (noise reduction) unit;

FIG. 10 is a flowchart for describing an example of the flow of noisereduction processing;

FIG. 11 is a diagram for describing an example of the way in which noisereduction processing is performed;

FIG. 12 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 13 is a diagram for describing an example of parallax enhancement;

FIG. 14 is a diagram for describing an example of parallax enhancement;

FIG. 15 is a block diagram illustrating a primary configuration exampleof a parallax enhancing unit;

FIG. 16 is a diagram for describing an example of parallax enhancement;

FIG. 17 is a diagram for describing an example of parallax enhancement;

FIG. 18 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 19 is a flowchart for describing an example of the flow of parallaxenhancing processing;

FIG. 20 is a block diagram illustrating a primary configuration exampleof an SR unit;

FIG. 21 is a flowchart for describing an example of the flow ofsuper-resolution processing;

FIG. 22 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 23 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 24 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 25 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 26 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 27 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 28 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 29 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 30 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 31 is a block diagram illustrating a primary configuration exampleof an SR-NR unit;

FIG. 32 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 33 is a flowchart for describing an example of the flow of SRNRprocessing;

FIG. 34 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 35 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 36 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 37 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 38 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 39 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 40 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 41 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 42 is a block diagram illustrating a primary configuration exampleof an image processing device;

FIG. 43 is a flowchart for describing an example of the flow of imageprocessing;

FIG. 44 is a block diagram illustrating a primary configuration exampleof an imaging apparatus; and

FIG. 45 is a block diagram illustrating a primary configuration exampleof a personal computer.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments for carrying out the present disclosure will now bedescribed. Description will proceed in the following order.

1. First Embodiment (Image Processing Device)

2. Second Embodiment (Imaging Apparatus)

3. Third Embodiment (Personal Computer)

1. First Embodiment Light Field Camera

First, description will be made of a light field camera. A light fieldcamera is an imaging apparatus using a technique called light fieldphotography, such as described in Japanese Unexamined Patent ApplicationPublication No. 2009-165115, for example. As illustrated in FIG. 1, alight field camera includes, for example, a main lens 11, a microlensarray 12, and an image sensor 13 which receives incident light via themain lens 11 and microlens array 12 and performs photoelectricconversion thereof. As illustrated in FIG. 1, the microlens array 12 isprovided at the focal position of the main lens 11, and the image sensor13 is provided at the focal position of the microlens array 12.

The microlenses of the microlens array 12 are each provided as tomultiple pixels of the image sensor 13. An example of the relationbetween pixel groups of the image sensor 13 and the microlens array 12is illustrated to the upper side of FIG. 2.

In FIG. 2, each square represents a pixel of the image sensor 13, andthe circles represent the microlenses of the microlens array 12. Forexample, a pixel 21 indicated by hatching represents a blue pixel (B)where a blue filter is provided, a pixel 22 indicated by shadingrepresents a green pixel (G) where a green filter is provided, and apixel 23 indicated by being blank represents a red pixel (R) where a redfilter is provided. Also, in FIG. 2, the image sensor 13 is illustratedas being nine pixels vertically by twelve pixels horizontally, and themicrolens array 12 is illustrated as being made up of three microlensesvertically and four horizontally. As a matter of course, the number ofmicrolenses which the microlens array 12 has, the number of pixels ofthe image sensor 13, and the placement of color filters, can bedetermined as suitable.

In the case of the example illustrated to the upper side of FIG. 2, onemicrolens is assigned to every three by three pixels (nine pixels) ofthe image sensor 13. In the example in FIG. 2, each of the pixelscorresponding to one microlens, i.e., each of the three by three pixelsupon which one circle is superimposed, receives incident light fromdifferent directions from each other by that microlens. That is to say,in the case of the example in FIG. 2, light from nine directions isreceived by the nine pixels. This holds true for the pixelscorresponding to the other microlenses as well.

Due to such a configuration, images of nine viewpoints are included inthe image data image obtained by the image sensor 13. For example, asindicated by arrows 31 through 34 in FIG. 2, pixel values of pixels atthe same position in each microlens are collected, whereby image dataobtained by performing photoelectric conversion of incident light fromthe same direction (i.e., an image of one certain direction) can beobtained.

In the case of the example in FIG. 2, such image processing (viewpointseparation) enables nine images each with different viewpoints to beobtained, as illustrated to the lower side of FIG. 2. Note that thenumber of pixels of the image sensor 13 corresponding to each microlensis optional. That is to say, with the image sensor 13, viewpoint imagesof a number equal to the number of pixels corresponding to eachmicrolens, are obtained.

A light field camera allows imaged images to be easily obtained whichinclude images of multiple viewpoints, which can be divided into imagesof each viewpoint at an optional timing, and from which can be extractedan image of a desired viewpoint.

Image Processing Device

Image processing to generate images regarding which 3D display can bemade (a right eye image and left eye image) from an imaged imageobtained from such a light field camera (multi-viewpoint image) will nowbe described.

FIG. 3 is a block diagram illustrating a primary configuration exampleof the image processing device. An image processing device 50illustrated in FIG. 3 performs image processing on an imaged imageimaged by and obtained from a light field camera (multi-viewpoint image)such as described with reference to FIGS. 1 and 2, and generates images(a right eye image and left eye image) for stereoscopic display(so-called 3D display).

As shown in FIG. 3, the image processing device 50 includes a viewpointseparating unit 51, a developing unit 52-1 and developing unit 52-2, acorrecting unit 53-1 and correcting unit 53-2, an SR (Super Resolution)unit 54, and an NR unit 55-1 and NR unit 55-2. In the followingdescription, in the event that the developing unit 52-1 and developingunit 52-2 do not have to be distinguished from each other, these will besimply referred to as “developing unit 52”. Also, in the event that thecorrecting unit 53-1 and correcting unit 53-2 do not have to bedistinguished from each other, these will be simply referred to as“correcting unit 53”. Further, in the event that the NR unit 55-1 and NRunit 55-2 do not have to be distinguished from each other, these will besimply referred to as “NR unit 55”.

The viewpoint separating unit 51 is input with a multi-viewpoint imagedimage obtained by a light field camera such as described above. Forexample, a moving image is shot with a light field camera, and the frameimages of the moving image are sequentially input to the viewpointseparating unit 51. From these input images, the viewpoint separatingunit 51 generates right eye images and left eye images for stereoscopicdisplay. For example, with the light field camera, three pixelsvertically by three pixels horizontally are assigned to each microlensas in the example in FIG. 2, and with the input image as well, threepixels vertically by three pixels horizontally make up one set, and eachset is also configured of images of nine different viewpoints. In thiscase, the viewpoint separating unit 51 extracts and collects the leftand right pixels of the middle tier from each three-pixel-by-three-pixelset, for example, thereby generating a right eye image (viewpoint Rimage) and a left eye image (viewpoint L image) for stereoscopicdisplay. The viewpoint separating unit 51 supplies the generatedviewpoint R image to the developing unit 52-1, and supplies theviewpoint L image to the developing unit 52-2. In the event that theinput images are the frame images of a moving image, the viewpointseparating unit 51 performs the above-described processing on each ofthe frame images.

The developing unit 52 performs developing processing for convertingimage data (RAW data) supplied from the viewpoint separating unit 51into a format according to a predetermined standard. The developing unit52-1 develops the data of the viewpoint R image and supplies to thecorrecting unit 53-1. The developing unit 52-2 develops the data of theviewpoint L image and supplies to the correcting unit 53-2. In the eventthat the input images are the frame images of a moving image, thedeveloping unit 52 performs the above-described developing processing oneach of the frame images.

The correcting unit 53 subjects the developed image data to optionalimage correction, such as for example, correction of exposure,luminance, contrast, saturation, sharpness, and so forth. The correctingunit 53-1 corrects the data of the viewpoint R image, and supplies thisto the SR unit 54. The correcting unit 53-2 corrects the data of theviewpoint L image, and supplies this to the SR unit 54. In the eventthat the input images are the frame images of a moving image, thecorrecting unit 53 performs the above-described correction processing oneach of the frame images.

As can be seen from the example in FIG. 2, the images subjected toviewpoint separation have lower resolution than the imaged image. Thatis to say, the viewpoint R image and viewpoint L image arelow-resolution images. Accordingly, the SR unit 54 performssuper-resolution processing on the supplied viewpoint R image andviewpoint L image so that the resolution thereof is high resolution. TheSR unit 54 makes high-resolution the viewpoint R image and viewpoint Limage by using multiple frame image, or by using images of multipleviewpoints (i.e., using multiple images). The SR unit 54 supplies theviewpoint R image data of which the resolution has been raised to highresolution to the NR unit 55-1. Also, the SR unit 54 supplies theviewpoint L image data of which the resolution has been raised to highresolution to the NR unit 55-2. The SR unit 54 performs suchsuper-resolution processing on each frame image.

In the case of a light field camera, light via the microlens is input toeach pixel, so there may be pixels which do not receive sufficientlight. Accordingly, an imaged image obtained by the light field cameratends to have a greater amount of noise. Accordingly, the NR unit 55performs noise reduction processing on the image of which the resolutionhas been raised to high resolution, so as to reduce noise. The NR unit55-1 performs noise reduction processing on the viewpoint R image data,and output the processing result thereof (data of the viewpoint R imagethat has been subjected to noise reduction processing). The NR unit 55-2performs noise reduction processing on the viewpoint L image data, andoutput the processing result thereof (data of the viewpoint L image thathas been subjected to noise reduction processing). In the event that theinput images are the frame images of a moving image, the NR unit 55performs the above-described noise reduction processing on each of theframe images.

SR Unit

Next, the SR unit 54 will be described. FIG. 4 is a block diagramillustrating a primary configuration example of the SR unit 54. As shownin FIG. 4, the SR unit 54 includes a viewpoint R super-resolutionprocessing unit 60-1 which performs super-resolution processing of dataof the viewpoint R image, and a viewpoint L super-resolution processingunit 60-2 which performs super-resolution processing of data of theviewpoint L image. In the following description, in the event that theviewpoint R super-resolution processing unit 60-1 and viewpoint Lsuper-resolution processing unit 60-2 do not have to be distinguishedfrom each other, these will be simply referred to as “super-resolutionprocessing unit 60”.

As shown in FIG. 4, the SR unit 54 performs super-resolution processingon both the input viewpoint R image and viewpoint L image, and outputsthe processing result (viewpoint R processing result and viewpoint Lprocessing result) of each. While the processing of each is basicallythe same, the viewpoint R super-resolution processing unit 60-1 performssuper-resolution processing with the viewpoint R image as an initialimage (standard image), and the viewpoint L super-resolution processingunit 60-2 performs super-resolution processing with the viewpoint Limage as an initial image (standard image).

Super-Resolution Processing Unit

FIG. 5 is a block diagram illustrating a primary configuration exampleof the super-resolution processing unit 60 in FIG. 4. As shown in FIG.5, the super-resolution processing unit 60 has, or more precisely, theviewpoint R super-resolution processing unit 60-1 and viewpoint Lsuper-resolution processing unit 60-2 each have, an initial imagegenerating unit 61, a switch 62, a super-resolution processing unit 63,and a convergence determining unit 64.

The super-resolution processing unit 60 is input with multiplelow-resolution images (g1, g2, . . . gn), and outputs onehigh-resolution image. These multiple low-resolution images (g1, g2, . .. gn) may be images of multiple frames of a current frame at a viewpointof interest to be processed (frame of interest) and frames in the pastfrom this frame of interest (past frames), or may be multiple viewpointimages of the frame of interest, including the viewpoint of interest.The low-resolution image g1 is the image of the viewpoint of interest inthe frame of interest.

The initial image generating unit 61 sets the low-resolution image g1 asthe initial value of the super-resolution processing result (initialimage) and stores this.

The switch 62 switches the supply source of images supplied to thesuper-resolution processing unit 63. With the super-resolutionprocessing unit 63, the same processing is repeatedly executed, with theswitch 62 connecting the output of the initial image generating unit 61to the super-resolution processing unit 63 just the first time ofexecution and connecting the output of the convergence determining unit64 to the super-resolution processing unit 63 all other times. That isto say, the initial image set at the initial image generating unit 61(i.e., the low-resolution image g1) is supplied to the super-resolutionprocessing unit 63 just the first time of repeated processing at thesuper-resolution processing unit 63, and from the second time on, theprocessing result from the previous time is supplied to thesuper-resolution processing unit 63.

The super-resolution processing unit 63 uses n low-resolution images g1,g2, and so on through gn, and also a user setting value α, to performsuper-resolution processing of images supplied from the switch 62, andsupplies the processing results (high-resolution images) thereof to theconvergence determining unit 64.

The convergence determining unit 64 determines whether sufficientconvergence has been performed with regard to the super-resolutionprocessing results (high-resolution images) supplied from thesuper-resolution processing unit 63. In the event that determination ismade that sufficient convergence has been performed with regard to asuper-resolution processing result, the convergence determining unit 64externally outputs the super-resolution processing result(high-resolution image), and stops processing. Also, in the event thatdetermination is made that sufficient convergence has not beenperformed, the convergence determining unit 64 supplies thesuper-resolution processing result (high-resolution image) to the switch62. In this case, super-resolution processing is performed by thesuper-resolution processing unit 63 on the processing result from theprevious time (high-resolution image).

Super-Resolution Processing Unit

FIG. 6 is a block diagram illustrating a primary configuration exampleof the super-resolution processing unit 63 in FIG. 5. As shown in FIG.6, the super-resolution processing unit 63 includes a high-frequencyestimation unit 71-1 through high-frequency estimation unit 71-n, andadding unit 72, an image quality control unit 73, a multiplier 74, anadding unit 75, a scale calculating unit 76, a multiplier 77, and asubtracting unit 78. In the following description, in the event that thehigh-frequency estimation unit 71-1 through high-frequency estimationunit 71-n do not have to be distinguished from each other, these will besimply referred to as “high-frequency estimation unit 71”.

The high-frequency estimation unit 71-1 through high-frequencyestimation unit 71-n each perform calculation of correction values forrestoring the high-frequency component of the image, using an image outof the low-resolution images g1 through gn corresponding to itself, andan image of reconstruction partway results supplied from the switch 62(including the initial image). For example, the high-frequencyestimation unit 71-1 calculates correction values using an image ofreconstruction partway results (including the initial image), and thelow-resolution image g1. The high-frequency estimation unit 71-2calculates correction values using the image of reconstruction partwayresults (including the initial image), and the low-resolution image g2.The high-frequency estimation unit 71-n calculates correction valuesusing the image of reconstruction partway results (including the initialimage), and the low-resolution image gn. The high-frequency estimationunit 71-1 through high-frequency estimation unit 71-n supply theirrespective processing results to the adding unit 72. The adding unit 72adds the processing results of the high-frequency estimation unit 71-1through high-frequency estimation unit 71-n, and supply this to theadding unit 75.

The image quality control unit 73 uses the image of reconstructionpartway results (including the initial image) supplied from the switch62 to calculate control values for pixel values, to achieve an idealimage based on a prior probability model of the image. The image qualitycontrol unit 73 supplies the processing result to the multiplier 74.

The multiplier 74 multiples the output of the image quality control unit73 by the user setting value α. Multiplying by the user setting value αallows the user to optionally control the final image quality of theimage. Note that an arrangement may be made where a predetermined fixedvalue is multiplied instead of the user setting value α, and further,this multiplication may be omitted completely. The multiplier 74supplies the multiplication result to the adding unit 75.

The adding unit 75 adds the multiplication result supplied from themultiplier 74 to the addition result supplied from the adding unit 72.The adding unit 75 supplies this addition result to the scalecalculating unit 76 and multiplier 77.

The scale calculating unit 76 determines a scale value for the finalcontrol value, using the image of reconstruction partway results(including the initial image) supplied from the switch 62, and the pixelvalue control signal (control value) supplied from the adding unit 75.The scale calculating unit 76 supplies the determined scale value to themultiplier 77.

The multiplier 77 multiplies the pixel value control signal (controlvalue) supplied from the adding unit 75 by the scale value supplied fromthe scale calculating unit 76, and supplies the multiplication result tothe subtracting unit 78. The subtracting unit 78 subtracts themultiplication result supplied from the multiplier 77, from the image ofreconstruction partway results (including the initial image) suppliedfrom the switch 62, and supplies the subtraction result to theconvergence determining unit 64.

High-Frequency Estimation Unit

The convergence determining unit 64 (FIG. 5) determines whether or notthe super-resolution convergence expression in the following Expression(1), for example, will hold. That is to say, the super-resolutionprocessing unit 63 (FIG. 5) calculates the right side of the followingExpression (1).

$\begin{matrix}\begin{matrix}{f_{m + 1} = {f_{m} - {\beta \frac{\partial{E\left( f_{m} \right)}}{\partial f}}}} \\{= {f_{m} - {\beta \left( {{\sum\limits_{k = 1}^{K}\; {W_{k}^{T}H^{T}{D^{T}\left( {{{DHW}_{k}f_{m}} - g_{k}} \right)}}} + {\alpha \; L^{T}{Lf}_{m}}} \right)}}}\end{matrix} & (1)\end{matrix}$

The high-frequency estimation units 71 each perform processingequivalent to the portion within the summation.

FIG. 7 is a block diagram illustrating a primary configuration exampleof a high-frequency estimation unit 71 in FIG. 6. As shown in FIG. 7,the high-frequency estimation unit 71 has a motion detecting unit 80, amotion compensation unit 81, a spatial filter 82, a downsampling unit83, a subtracting unit 84, an upsampling unit 85, an inverse spatialfilter 86, an inverse motion compensation unit 87, and a resolutionconversion unit 88.

The motion detecting unit 80 detects motion (motion vector) between thehigh-resolution image supplied from the switch 62, and a low-resolutionimage gk (where 1≦k≦n). The low-resolution image gk is supplied from thecorrecting unit 53, or from a buffer (unshown) for holdinglow-resolution images supplied from the correcting unit 53. These imageshave different resolution from each other. Accordingly, for the sake ofconvenience, the low-resolution image gk is upsampled by the resolutionconversion unit 88 to the resolution of the high-resolution imagesupplied from the switch 62, and then supplied to the motion detectingunit 80. The motion detecting unit 80 supplies the detected motionvector to the motion compensation unit 81 and inverse motioncompensation unit 87.

The motion compensation unit 81 uses the motion vector supplied from themotion detecting unit 80 to perform motion compensation on thehigh-resolution image supplied from the switch 62. This processingcorresponds to the computation of Wk in the above Expression (1). Themotion compensation unit 81 supplies the motion compensation results(the high-resolution image regarding which positioning has beenperformed) to the spatial filter 82.

The spatial filter 82 performs processing of simulating deterioration inspatial resolution regarding the high-resolution image supplied from themotion compensation unit 81. Note that here, convolution is performedregarding the image, with a pre-measured point spread function as afilter. This processing corresponds to the computation of H in the aboveExpression (1). The spatial filter 82 supplies the high-resolution imagethat has been subjected to filter processing to the downsampling unit83.

The downsampling unit 83 downsamples the high-resolution image suppliedfrom the spatial filter 82 to the resolution of the input image(low-resolution image gk). This processing corresponds to thecomputation of D in the above Expression (1). The downsampling unit 83supplies the high-resolution image of which the resolution has beenlowered (i.e., low-resolution image) to the subtracting unit 84.

The subtracting unit 84 subtracts the low-resolution image gk from thelow-resolution image supplied from the downsampling unit 83, andsupplies the subtraction results thereof (difference image) to theupsampling unit 85.

The upsampling unit 85 performs upsampling of the difference imagesupplied from the subtracting unit 84 so as to correspond to thedownsampling performed by the downsampling unit 83. That is to say, theresolution of the difference image is raised to the resolution of thehigh-resolution image supplied from the switch 62. This processingcorresponds to the computation of Dt in the above Expression (1), and isupsampling processing with zero insertion. The upsampling unit 85supplies the difference image of which the resolution has been raised tohigh resolution to the inverse spatial filter 86.

The inverse spatial filter 86 performs calculation of correlation of thehigh-resolution difference image supplied from the upsampling unit 85 asto the point spread function PSF used at the spatial filter 82. Thisprocessing corresponds to the computation of Ht in the above Expression(1). The inverse spatial filter 86 supplies this high-resolutiondifference image that has been subjected to inverse spatial filterprocessing to the inverse motion compensation unit 87.

The inverse motion compensation unit 87 uses the motion vector suppliedfrom the motion detecting unit 80 to perform positioning of thedifference image supplied from the inverse spatial filter 86 in theopposite direction as to that of the motion compensation performed bythe motion compensation unit 81, i.e., inversion motion compensation.That is to say, this inverse compensation offsets the positioning by themotion compensation unit 81. The inverse motion compensation unit 87supplies the difference mage subjected to this inverse motioncompensation to the adding unit 72.

The resolution conversion unit 88 upsamples the low-resolution image gksupplied thereto to the resolution of the high-resolution image suppliedfrom the switch 62. The resolution conversion unit 88 then supplies thisupsampled low-resolution image gk to the motion detecting unit 80.

Image Quality Control Unit

The image quality control unit 73 performs processing corresponding tothe portion of LTLfm at the second term in the right side of the aboveExpression (1). FIG. 8 is a block diagram illustrating a primaryconfiguration example of the image quality control unit 73 shown in FIG.6. As shown in FIG. 8, the image quality control unit 73 has a Laplacianoperator 89-1 and Laplacian operator 89-2. In the following description,in the event that the Laplacian operator 89-1 and Laplacian operator89-2 do not have to be distinguished from each other, these will besimply referred to as “Laplacian operator 89”.

The Laplacian operator 89-1 and Laplacian operator 89-2 which areserially connected are processing units which are both the same, andperform the same processing. That is to say, the Laplacian operator 89performs Laplacian operation on the high-resolution image supplied fromthe switch 62 twice. The Laplacian operator 89 which has performedprocessing twice supplies the processing result to the multiplier 74.

NR Unit

Next, the NR unit 55 will be described. FIG. 9 is a block diagramillustrating a primary configuration example of the NR unit 55 shown inFIG. 3. As shown in FIG. 9, the NR unit 55 includes frame memory 91, amotion prediction processing unit 92, a motion compensation processingunit 93, an addition determining unit 94, and an addition processingunit 95.

The frame memory 91 stores the input image supplied from the SR unit 54that has been subjected to super-resolution processing, and an imagesupplied from the addition processing unit 95 that has been subjected tonoise reduction processing (hereinafter also referred to as “NR image”).At a predetermined timing, or based on a request from another unit, theframe memory 91 supplies the NR image stored therein to the motionprediction processing unit 92, an addition determining unit 94, and anaddition processing unit 95, as a reference image. Also, the framememory 91 supplies the input image stored therein to the motionprediction processing unit 92 as a reference image, either at apredetermined timing or under request from another.

The motion prediction processing unit 92 performs motion predictionusing the standard image and reference image supplied from the framememory 91, and detects a motion vector between the images. The motionprediction processing unit 92 supplies the detected motion vector(hereinafter, also referred to simply as “MV”) and the reference imageto the motion compensation processing unit 93.

The motion compensation processing unit 93 performs motion compensationof the reference image supplied from the motion prediction processingunit 92, using the motion vector supplied from the motion predictionprocessing unit 92, thereby generating a motion compensation image(hereinafter also referred to as “MC image”). The motion compensationprocessing unit 93 supplies the motion compensation image to the anaddition determining unit 94 and addition processing unit 95.

The an addition determining unit 94 obtains image information regardingthe image to be processed, such as shooting conditions and so forth,from outside the NR unit 55. The an addition determining unit 94performs addition determining processing for determining pixelsregarding which the standard image supplied from the frame memory 91 andthe motion compensation image supplied from the motion compensationprocessing unit 93 are to be added, based on the image information. Thean addition determining unit 94 supplies the addition determinationresults thereof to the addition processing unit 95 as an additiondetermination map.

The addition processing unit 95 adds the standard image supplied fromthe frame memory 91 and the motion compensation image supplied from themotion compensation processing unit 93, following the additiondetermination map supplied from the an addition determining unit 94. Theaddition processing unit 95 outputs the addition processing results (theNR processing results, i.e., an NR image), and also supplies this to theframe memory 91 so as to be stored.

Flow of Noise Reduction Processing

FIG. 10 is a flowchart for describing an example of the flow of noisereduction processing executed by this NR unit 55. Upon noise reductionprocessing being started, in step S11, the motion prediction processingunit 92 performs motion prediction processing using the standard imageand reference image. The motion compensation processing unit 93 performsmotion compensation on the reference image using the motion vector thathas been detected, and generates a motion compensation image.

In step S12, the addition determining unit 94 performs additiondetermination processing based on the standard image and motioncompensation image, and generates an addition determination map.

In step S13, the addition processing unit 95 adds the standard image andmotion compensation image following the addition determination mapgenerated in step S12.

In step S14, the addition processing unit 95 determines whether or not areference image of the next point-in-time exists, and in the event thatpositive determination is made, the flow returns to step S11 andsubsequent processing is repeated. That is to say, the NR unit 55executes the processing of steps S11 through S13 as to each frame image,and performs noise reduction as to each frame image.

In the event that determination is made in step S14 that no referenceimage of the next point-in-time exists (i.e., that non new input imagewill be input), the noise reduction processing is ended.

A more specific example will be described with reference to FIG. 11. Forexample, an NR image from one frame back, which will be referred to as“standard image a”, and the input image of the frame of interest, whichwill be referred to as “reference image A”, are used to perform motionprediction and motion compensation (step S11-1). This generates a motioncompensation image 96-1. Addition determination processing is thenperformed using the standard image a and motion compensation image 96-1(step S12-1). This generates an addition determination map 97-1. Next,the arithmetic mean of the standard image a and the motion compensationimage 96-1 is calculated following the addition determination map 97-1(step S13-1). Thus, an NR image 98-1 is generated and output. This NRimage 98-1 is used for the NR processing as to the next frame, asstandard image b.

When the frame of interest transitions to the next frame, the inputimage of the frame of interest, which will be referred to as “referenceimage B”, and the standard image b, are used to perform motionprediction and motion compensation (step S11-2). This generates a motioncompensation image 96-2. Addition determination processing is thenperformed using the standard image b and motion compensation image 96-2(step S12-2). This generates an addition determination map 97-2. Next,the arithmetic mean of the standard image b and the motion compensationimage 96-2 is calculated following the addition determination map 97-2(step S13-2). Thus, an NR image 98-2 is generated and output. This NRimage 98-2 is used for the NR processing as to the next frame, asstandard image c.

When the frame of interest transitions to the next frame, the inputimage of the frame of interest, which will be referred to as “referenceimage C”, and the standard image c, are used to perform motionprediction and motion compensation (step S11-3). This generates a motioncompensation image 96-3. Addition determination processing is thenperformed using the standard image c and motion compensation image 96-3(step S12-3). This generates an addition determination map 97-3. Next,the arithmetic mean of the standard image c and the motion compensationimage 96-3 is calculated following the addition determination map 97-3(step S13-3). Thus, an NR image 98-3 is generated and output. Such noisereduction processing is repeated for each frame.

However, in this case, the amount of parallax of the multi-viewpointimage is dependent on the aperture of the diaphragm of the imaging lens,and the number of pixels of the imaging device assigned to themicrolenses. Accordingly, with multi-viewpoint images obtained usingsuch an imaging apparatus, generally, restriction in size of the imaginglens and suppression in deterioration of resolution has led toinsufficient amount of parallax, so sufficiently great visualstereoscopic effect (sense of depth) has been difficult.

Image Processing Device

FIG. 12 is a block diagram illustrating a primary configuration exampleof an image processing device. The image processing device 100 shown inFIG. 12 performs image processing on an imaged image obtained from alight field camera (multi-viewpoint image), and generates images (aright eye image and left eye image) for stereoscopic display (so-called3D display), in the same way as with the image processing device 50.

Note however, that with the image processing device 100, stereoscopiceffect of multi-viewpoint images can be controlled. That is to say, withthe image processing device 100, a more suitable degree of stereoscopiceffect (sense of depth) of a stereoscopic display can be obtained bycontrolling the amount of parallax of right eye images and left eyeimages generated.

As shown in FIG. 12, the image processing device 100 includes aviewpoint separating unit 101, a developing unit 102-1 throughdeveloping unit 102-4, a correcting unit 103-1 through correcting unit103-4, an SR unit 104, an NR unit 105-1 and NR unit 105-2, and aparallax enhancing unit 106.

Note that in the following description, in the event that the developingunit 102-1 through developing unit 102-4 do not have to be distinguishedfrom each other, these will be simply referred to as “developing unit102”. Also, in the following description, in the event that thecorrecting unit 103-1 through correcting unit 103-4 do not have to bedistinguished from each other, these will be simply referred to as“correcting unit 103”. Further, in the event that the NR unit 105-1 andNR unit 105-2 do not have to be distinguished from each other, thesewill be simply referred to as “NR unit 105”.

The viewpoint separating unit 101 is a processing unit basically thesame as with the viewpoint separating unit 51, and separates an inputimage into each viewpoint and generates images for all viewpoints. Thatis to say, the viewpoint separating unit 101 separates multi-viewpointimage data, including images of multiple viewpoints and representingintensity distribution of light and the direction of travel of lightaccording to the positions and pixel values of the pixels, intoindividual viewpoints, thereby generating a plurality ofsingle-viewpoint image data. In the case of the example in FIG. 12, theviewpoint separating unit 101 separates the imaged image into fourviewpoint images.

The viewpoint separating unit 101 supplies each viewpoint image that hasbeen separated (single-viewpoint image data) to the correspondingdeveloping unit 102. In the case of the example in FIG. 12, theviewpoint separating unit 101 supplies the four viewpoint images to thedeveloping unit 102-1 through developing unit 102-4.

The developing unit 102 is a processing unit basically the same as thedeveloping unit 52, performing developing processing on a RAW image(single-viewpoint image data) supplied thereto, and supplying theprocessing results to a correcting unit 103 corresponding to itself. Thecorrecting unit 103 is a processing unit basically the same as thecorrecting unit 53, performing correction processing on the suppliedimage (single-viewpoint image data), and supplying the processingresults to the SR unit 104.

Note that FIG. 12 shows four each of the developing unit 102 andcorrecting unit 103, for sake of convenience, but one each of thedeveloping unit 102 and correcting unit 103 is provided for eachviewpoint. For example, in the case of the example in FIG. 2, the imagedimage includes nine viewpoint images. In this case, there are providednine each of the developing unit 102 and correcting unit 103. Theviewpoint separating unit 101 separates the imaged image into individualviewpoints and generates nine images, with the nine developing units 102and correcting units 103 each processing different viewpoint images.

The SR unit 104 has basically the same configuration as the SR unit 54,and performs super-resolution processing in the same way. That is tosay, the SR unit 104 performs super-resolution processing to raise theresolution of images of the single-viewpoint image data suppliedthereto. More specifically, the SR unit 104 performs super-resolutionprocessing using images of all viewpoints supplied thereto aslow-resolution images g1, g2, g3, and so on through gn, so as togenerate a two-viewpoint high-resolution image (i.e., a viewpoint Rimage and a viewpoint L image). The SR unit 104 supplies the generatedhigh-resolution image viewpoint R image to the NR unit 105-1, andsupplies the high-resolution image viewpoint L image to the NR unit105-2.

The NR unit 105 is a processing unit basically the same as the NR unit55, and performs the same noise reduction processing. That is to say,the NR unit 105 performs noise reduction processing to reduce noise foreach of the multiple single-viewpoint image data. The NR unit 105supplies an NR image, which is a processing result, to the parallaxenhancing unit 106.

The parallax enhancing unit 106 corrects the parallax amount of theviewpoint R image and viewpoint L image supplied thereto, so as toenhance or decrease the parallax. That is to say, the parallax enhancingunit 106 controls the amount of parallax between the multiplesingle-viewpoint image data. The parallax enhancing unit 106 outputsimages of each viewpoint subjected to amount-of-parallax correction.

Parallax Enhancement

Next, a method of parallax enhancement will be described. To begin with,stereoscopic effect which a user senses occurs due to the position of anobject in a right eye image (viewpoint R image) primarily viewed withthe right eye and in a left eye image (viewpoint L image) primarilyviewed with the left eye being different. The difference in position ofthe object common to the two images is parallax. The greater thisparallax is, the greater the stereoscopic effect (sense of depth) thatthe user viewing the stereoscopically displayed image senses. Forexample, by placing a common object further to the right in theviewpoint R image and further to the left in the viewpoint L image, thedeeper (farther) the position of the object which the user senses(perceived image position) is. Conversely, by placing a common objectfurther to the left in the viewpoint R image and further to the right inthe viewpoint L image, the nearer (closer) the position of the objectwhich the user senses (perceived image position) is. If the position ofthe object in the viewpoint R image is farther left than the position inthe viewpoint L image, the position of the object sensed (perceivedimage position) is nearer than a case where the position is displayedflat (a case of zero parallax).

Accordingly, in a case of displaying a perceived image of an object at adeeper position, the parallax enhancing unit 106 moves the image of theobject in the viewpoint R image farther to the right, and converselymoves the image of the object in the viewpoint L image farther to theleft, as shown above in FIG. 13. On the other hand, in a case ofdisplaying a perceived image of an object at a nearer position, theparallax enhancing unit 106 moves the image of the object in theviewpoint R image farther to the left, and conversely moves the image ofthe object in the viewpoint L image farther to the right, as shown belowin FIG. 13.

Thus, the perceived image of the object moves nearer than the displayplane or deeper than the display plane, as shown in FIG. 14. That is tosay, by controlling the amount of parallax, the parallax enhancing unit106 can control the stereoscopic effect (sense of depth) of the imagedisplayed stereoscopically. By the stereoscopic effect of the imagebeing controlled by the parallax enhancing unit 106, a stereoscopicdisplay image with a predetermined stereoscopic effect (viewpoint Rimage and viewpoint L image having sufficient amount of parallax) can beobtained at the image processing device 100.

Parallax Enhancing Unit

FIG. 15 is a block diagram illustrating a primary configuration exampleof the parallax enhancing unit 106 in FIG. 12. As shown in FIG. 15, theparallax enhancing unit 106 includes a disparity detecting unit 111, adistance information generating unit 112, a derivative signal generatingunit 113, a correcting unit 114, a selector 115, an adding unit 116, asubtracting unit 117, and a selector 118.

The disparity detecting unit 111 detects disparity, which is a parameterdirectly or indirectly indicating the amount of parallax between aviewpoint R image supplied from the NR unit 105-1 and a viewpoint Limage supplied from the NR unit 105-2. The disparity detecting unit 111supplies the detected disparity to the distance information generatingunit 112.

The distance information generating unit 112 identifies the currentdistance from the user to the perceived image of the object to becorrected, based on the disparity between the two images supplied fromthe disparity detecting unit 111, and based on this current distance anda target position, obtains a correction amount for the derivativesignal. The distance information generating unit 112 supplies theobtained distance information to the correcting unit 114.

The derivative signal generating unit 113 performs derivation of theviewpoint R image (or the viewpoint L image, either will suffice), andgenerates a derivative signal. The derivative signal generating unit 113supplies the generated derivative signal to the correcting unit 114.

The correcting unit 114 corrects the derivative signal supplied from thederivative signal generating unit 113, using the correction amountsupplied from the distance information generating unit 112. Thiscorrection of the derivative signal corrects the amount of movement ofthe object in the viewpoint R image and viewpoint L image for parallaxenhancement. The correcting unit 114 supplies the derivative signalfollowing correction to the adding unit 116 and subtracting unit 117.

The selector 115 and selector 118 control whether to supply theinput/output viewpoint R image to the adding unit 116 or the subtractingunit 117. That is to say, switching of this input/output controlswhether the derivative signal following correction will be added to orsubtracted from the viewpoint R image and viewpoint L image.

The adding unit 116 adds the corrected derivative signal supplied fromthe correcting unit 114 to the viewpoint R image or viewpoint L imageinput via the selector 115, thereby correcting the viewpoint R image orviewpoint L image. The adding unit 116 externally outputs the imagefollowing correction from the parallax enhancing unit 106 via theselector 118, as a viewpoint R image or viewpoint L image.

The subtracting unit 117 subtracts the corrected derivative signalsupplied from the correcting unit 114 from the viewpoint R image orviewpoint L image input via the selector 115. This corrects theviewpoint R image or viewpoint L image. Thus, the subtracting unit 117externally outputs the image following correction from the parallaxenhancing unit 106 via the selector 118, as a viewpoint R image orviewpoint L image.

The way in which correction is made will be described with reference toFIG. 16. For example, let us say that an image with a waveform such asshown at the top tier in FIG. 16 (with zero parallax) is input to theparallax enhancing unit 106 as a viewpoint R image and viewpoint Limage. The derivative signal generating unit 113 performs derivation ofthis signal to generate a derivative signal such as shown in the secondtier from the top in FIG. 16. In this case, the derivative signal has apositive value at the leading edge of the input signal (section A), andhas a negative value at the trailing edge of the input signal (sectionB).

The adding unit 116 adds this derivative signal to the input signal,thereby generating a left eye image signal such as shown at the bottomtier in FIG. 16. In this case, the left eye image signal has its highluminance region shifted to the left as compared to the input signal.

The subtracting unit 117 subtracts this derivative signal from the inputsignal, thereby generating a right eye image signal such as shown at thethird tier from the top in FIG. 16. In this case, the right eye imagesignal has its high luminance region shifted to the right as compared tothe input signal.

Thus, the parallax enhancing unit 106 can easily control the position ofthe high luminance region, i.e., the position of the object, that is tosay the amount of parallax, by adding/subtracting the derivative signalto/from the input image. Note that the correcting unit 114 controls theamount of parallax by multiplying the derivative signal by the amount ofcorrection. That is to say, this amount of correction controls thebreadth of non-zero portions of the derivative signal (A and B).

The derivative signal generating unit 113 generates a derivative signalfrom each frame, as shown in FIG. 17. That is to say, derivation isperformed if the input image for each frame by the derivative signalgenerating unit 113. also, the adding unit 116 and subtracting unit 117generate a right eye image and left eye image for each frame.

Flow of Image Processing

An example of the flow of image processing executed by the imageprocessing device 100 in FIG. 12 will be described with reference to theflowchart in FIG. 18.

Upon image processing being started, in step S101 the viewpointseparating unit 101 separates the input image into individualviewpoints.

In step S102, the developing unit 102 develops the images of theviewpoints generated in step S101.

In step S103, the correcting unit 103 corrects the images of theviewpoints developed by the processing in step S102.

In step S104, the SR unit 104 performs super-resolution processing usingthe images of the viewpoints corrected by the processing in step S103.

In step S105, the NR unit 105 performs noise reduction processing oneach of the viewpoint R image and viewpoint L image.

In step S106, the parallax enhancing unit 106 performs parallaxenhancement on the images that have been subjected to noise reduction inthe processing in step S105. Upon the parallax enhancement beingcompleted, the parallax enhancing unit 106 ends the image processing.

Next, an example of the flow of parallax enhancement processing executedin step S106 in FIG. 18 will be described with reference to theflowchart in FIG. 19.

Upon parallax enhancement processing being started, in step S121 thedisparity detecting unit 111 detects the disparity between the viewpointR image and viewpoint L image.

In step S122, the distance information generating unit 112 determinesthe amount of correction for the derivative signal, based on the size ofthe disparity detected in step S121.

In step S123, the derivative signal generating unit 113 generates aderivative signal from the input images (viewpoint R image and viewpointL image), and the correcting unit 114 corrects this derivative signal bythe correction amount determined in step S122.

In step S124, the selector 115 and selector 118 switch the connectionfor input/output of the adding unit 116 and subtracting unit 117 asappropriate, and select addition/subtraction of the derivative signal asto the input signals of each of the viewpoints.

As described with reference to FIGS. 13 and 14, with an imagestereoscopically displayed, the direction in which an object should bemoved in each viewpoint image differs, depending one whether thedepthwise position perceived by the user of the object in the image(position of perceived image) is to be moved nearer or is to be moveddeeper. Also, as described with reference to FIG. 16, the direction ofmovement of the object changes depending on whether the derivativesignal is added to the image or subtracted therefrom. Accordingly,whether to add or subtract the derivative signal is determined by thedirection in which the object is to be moved (i.e., whether the amountof parallax is to be increased or decreased).

In step S125, the adding unit 116 adds the derivative signal correctedin step S123 to the input signals of the viewpoint selected in step S124(the viewpoint R image or viewpoint L image).

In step S126, the subtracting unit 117 subtracts the derivative signalcorrected in step S123 from the input signals of the viewpoint selectedin step S124 (the viewpoint R image or viewpoint L image).

In step S127, the selector 118 outputs the addition result obtained bythe processing in step S125, and the subtraction result obtained by theprocessing in step S126, following the selection according to theprocessing in step S124, as respective correction results of eachviewpoint.

Regardless of whether the derivative signal is added to the viewpoint Rimage and the derivative signal is subtracted from the viewpoint Limage, or the derivative signal is subtracted from the viewpoint R imageand the derivative signal is added to the viewpoint L image, theselector 118 outputs the computation result for the viewpoint R image asthe correction result of the viewpoint R image, and outputs thecomputation result for the viewpoint L image as the correction result ofthe viewpoint L image.

Upon the processing of step S127 ending, the selector 118 ends theparallax enhancement processing, and returns the flow to FIG. 18.

By perform processing such as described above, the image processingdevice 100 can obtain a stereoscopic display image with a desiredstereoscopic effect (a viewpoint R image and viewpoint L image withsufficient amount of parallax).

Also, as described above, the SR unit 104 of the image processing device100 performs super-resolution processing using images of all viewpointsas low-resolution images g1, g2, g3, and so on through gn. Accordingly,in the event that the number of viewpoints of the image to be processedthat has been obtained by a light field camera is three or more, the SRunit 104 can perform super-resolution processing using low-resolutionimages of a number greater than the case of the SR unit 54 describedabove with reference to FIG. 5 and so forth, so the image quality of thesuper-resolution image results can be improved. That is to say, the SRunit 104 performs super-resolution processing also using images otherthan the two viewpoints for stereoscopic display (images other than theviewpoint R image and viewpoint L image), which had been simplydiscarded with the image processing device 50, thereby improving theusage efficiency of image data, whereby the image quality of the imageobtained as a result of processing can be improved.

Note that by increasing the number of multiple images in the temporaldirection, the number of low-resolution images g1, g2, g3, and so onthrough gn can also be increased, but this means that the images have tobe held. The amount of buffer to hold the extra images increasesaccordingly, meaning an increased load on hardware resources, which maylead to increased costs and larger circuit scale. The SR unit 104 canincrease the number of low-resolution images g1, g2, g3, and so onthrough gn within the frame of interest alone.

Note that in the above description, a developing unit 102 and correctingunit 103 is described as being provided for each of the viewpoints, thearrangement is not restricted to this, and an arrangement may be madewhere only a part of the viewpoint images are subjected to developingprocessing and correction processing. For example, in the example inFIG. 12, an arrangement may be made where the imaged image includesimages of nine viewpoints, but the viewpoint separating unit 101generates images of four viewpoints therefrom, which are supplied to thedeveloping unit 102-1 through developing unit 102-4. In this case, theimage quality is lower than a case of using all viewpoint images of thelow-resolution images g1, g2, g3, and so on through gn, used at the SRunit 104, but the image quality can be made to be higher than a case ofusing just two viewpoints.

Commonalizing Processing

While the image processing device 100 can control stereoscopic effect byproviding the parallax enhancing unit 106 as described above, the loadon image processing increases accordingly. That is to say, an increasedload is placed on hardware resources, which may lead to increased costs,larger circuit scale, increased processing time, and so forth.Accordingly, same processing which the processing units perform may becommonalized such that the processing results thereof are shared amongthe multiple processing units, so as to reduce the load of imageprocessing as much as possible.

SR Unit

For example, as described with reference to FIG. 4, the SR unit 104performs super-resolution processing for each of the viewpoint R imageand viewpoint L image, and disparity between the two images is detectedin the super-resolution processing of each. Accordingly, an arrangementmay be made where detection of this disparity is commonalized in thesuper-resolution processing of each viewpoint.

FIG. 20 is a block diagram illustrating a primary configuration exampleof the SR unit 104 in such a case. As shown in FIG. 20, the SR unit 104in this case has a disparity detecting unit 151, a viewpoint Rsuper-resolution processing unit 152-1, and a viewpoint Lsuper-resolution processing unit 152-2. Note that in the followingdescription, in the event that the viewpoint R super-resolutionprocessing unit 152-1 and viewpoint L super-resolution processing unit152-2 do not have to be distinguished from each other, these will besimply referred to as “super-resolution processing unit 152”.

The disparity detecting unit 151 detects disparity between viewpoints,independently from the super-resolution processing, using the images ofeach viewpoint, and supplies the detected disparity to the viewpoint Rsuper-resolution processing unit 152-1 and viewpoint L super-resolutionprocessing unit 152-2.

The viewpoint R super-resolution processing unit 152-1 performssuper-resolution processing regarding the viewpoint R image, using thedisparity supplied from the disparity detecting unit 151. The viewpointL super-resolution processing unit 152-2 performs super-resolutionprocessing regarding the viewpoint L image, using the disparity suppliedfrom the disparity detecting unit 151.

The super-resolution processing unit 152 basically has the sameconfiguration as the super-resolution processing unit 60, and performsthe same processing. However, unlike the case of the super-resolutionprocessing unit 60, the super-resolution processing unit 152 performssuper-resolution processing using images of all viewpoints as thelow-resolution images g1, g2, g3, and so on through gn. Also, while thesuper-resolution processing unit 60 performs motion detection betweenthe low-resolution images gk and the initial image or partway results ofsuper-resolution processing, and performs super-resolution processingusing the motion vector thereof, the super-resolution processing unit152 performs super-resolution processing using the disparity betweeneach of the viewpoints supplied from the disparity detecting unit 151.

That is to say, the processing of the disparity detecting unit 151 isequivalent to the processing of the motion detecting unit 80 (FIG. 7) inthe case of the SR unit 54. In other words, the processing which hadbeen performed for each viewpoint (processing which had been redundantlyperformed) in the case of the SR unit 54, is commonalized in the case ofthe SR unit 104 in the example in FIG. 20, and used for both thesuper-resolution processing performed on the viewpoint R image and thesuper-resolution processing performed on the viewpoint L image.Accordingly, the SR unit 104 in this case can reduce the load onsuper-resolution processing. That is to say, the image processing device100 can reduce the load of image processing. Accordingly, the imageprocessing device 100 can perform image processing using hardwareresources more efficiently, thereby suppressing increase in costs,larger circuit scale, increased processing time, and so forth.

Flow of Super-Resolution Processing

The flow of super-resolution processing performed in step S104 in FIG.18 by the SR unit 104 will be described with reference to the flowchartin FIG. 21.

Upon the super-resolution processing being started, in step S151 thedisparity detecting unit 151 detects the disparity between allviewpoints. In step S152, the viewpoint R super-resolution processingunit 152-1 performs super-resolution of viewpoint R using all view pointimages and the disparity detected in step S151. In step S153, theviewpoint L super-resolution processing unit 152-2 performssuper-resolution of viewpoint L using all view point images and thedisparity detected in step S151.

Upon the processing in step S153 ending, the viewpoint Lsuper-resolution processing unit 152-2 ends the super-resolutionprocessing, and the flow returns to FIG. 18. Thus, the SR unit 104 canreduce load of super-resolution processing.

Image Processing Device

While description has been made above that super-resolution processingis performed using only the images of the two viewpoints of viewpoint Rand viewpoint L for stereoscopic display, and further noise reductionprocessing and parallax enhancement is performed, and output is made, itshould be understood that the number of viewpoints of images to beoutput is optional. For example, all viewpoint images may be output. Inthis case, the super-resolution processing, noise reduction processing,and parallax enhancement processing, are each performed as to allviewpoint images.

FIG. 22 is a block diagram illustrating a primary configuration exampleof an image processing device. The image processing device 160 shown inFIG. 22 is the basically the same devices as the image processing device100, but unlike the image processing device 100 subjects all images tosuper-resolution processing, noise reduction processing, and parallaxenhancement processing, and outputs.

As shown in FIG. 22, the image processing device 160 has basically thesame configuration as the image processing device 100, but has a SR unit164 instead of the SR unit 104, and has a parallax enhancing unit 166instead of the parallax enhancing unit 106.

The SR unit 164 performs super-resolution processing as to all viewpointimages. That is to say, the SR unit 164 has super-resolution processingunits 60 for all viewpoints. Note that disparity detection may becommonalized in super-resolution processing as to all viewpoint images,as described with reference to FIG. 20. That is to say, an arrangementmay be made where the SR unit 164 has a disparity detecting unit 151,and further has super-resolution processing units 152 for allviewpoints.

The parallax enhancing unit 166 performs parallax enhancement betweenviewpoints for all viewpoints. That is to say, the parallax enhancingunit 166 has a configuration such as shown in FIG. 15, regarding allcombinations of two viewpoints (viewpoint pairs). The parallaxenhancement as to the viewpoint pairs may be performed individually, ormay be performed while adjusting one another.

Also, the image processing device 160 has an NR unit 105 for eachviewpoint (NR unit 105-1 through NR unit 105-4). In the case of theexample in FIG. 22, only a configuration of four viewpoints isillustrated in the same way as with the example in FIG. 12, but thedeveloping unit 102, correcting unit 103, and NR unit 105 are in factprovided for all viewpoint images. For example, in the case that thenumber of viewpoints is nine, as with the example in FIG. 2, nine eachare provided of the developing unit 102, correcting unit 103, and NRunit 105.

Due to such a configuration, the image processing device 160 can raisethe resolution of all viewpoints to high resolution and output.Accordingly, optional viewpoint images can be used at a downstreamprocessing unit which performs processing using the output imagesthereof. That is to say, the image processing device 160 can improve theusage efficiency of image data to be processed by image processing(multi-viewpoint image data).

Flow of Image Processing

An example of the flow of image processing in this case will bedescribed with reference to the flowchart in FIG. 23.

The processing of steps S161 through S163 is executed in the same way asthe processing of steps S101 through S103 in FIG. 18.

In step S164, the SR unit 164 performs super-resolution processing as toall viewpoint images.

In step S165, the NR units 105 perform noise reduction processing as toeach viewpoint image.

In step S166, the parallax enhancing unit 166 enhances parallax betweenall viewpoints.

Upon ending the processing in step S166, the parallax enhancing unit 166ends the image processing. By performing such processing, the imageprocessing device 160 can raise the resolution of all viewpoint imagesand output them.

Image Processing Device

Further, an arrangement may be made where desired viewpoint images areselected from all viewpoint images subjected to parallax enhancement,and output. FIG. 24 is a block diagram illustrating a primaryconfiguration example of an image processing device. The imageprocessing device 170 shown in FIG. 24 is basically the same device asthe image processing device 160. The image processing device 170 has, inaddition the configuration of the image processing device 160, aselector 177.

The selector 177 obtains all viewpoint images of which the resolutionhas been raised to high resolution, output from the parallax enhancingunit 166, selects desired viewpoint images therefrom (the selectionbeing made by the user, for example), and outputs these. While FIG. 24illustrates the selector 177 inputting four viewpoint images andselecting and outputting two viewpoint images therefrom, the number ofimages input/output to/from the selector 177 (number of viewpoints) isoptional, so long as the number of outputs does not exceed the number ofinputs.

Thus, the image processing device 170 can output desired viewpointimages. Note that a synthesizing processing unit which generates newviewpoint images using the multiple viewpoint images that have beeninput, may be provided instead of the selector 177. In this case, thesynthesizing unit can not only output new viewpoint images, but also canoutput a greater number of viewpoint images than the number of inputimages (number of viewpoints). In this way, optional processing unitsmay be provided downstream from the parallax enhancing unit 166.

Flow of Image Processing

An example of the flow of image processing in this case will bedescribed with reference to the flowchart in FIG. 25. The processing ofsteps S171 through S176 are executed in the same way as the processingof steps S161 through S166. In step S177, the selector 177 selectsviewpoint images to output. Upon the processing of step S177 ending, theselector 177 ends the image processing. By performing processing thus,the image processing device 170 can output desired viewpoint images.

Image Processing Device

While description has been made above with reference to FIG. 20 forexample, that disparity is detected at the SR unit, an arrangement maybe made wherein the detected disparity is used at the parallaxenhancement unit. FIG. 26 is a block diagram illustrating a primaryconfiguration example of an image processing device. The imageprocessing device 200 illustrated in FIG. 26 is basically the samedevice as the image processing device 100, basically has the sameconfiguration, and performs the same processing. Note however, that theimage processing device 200 has an SR unit 204 instead of the SR unit104, and a parallax enhancing unit 206 instead of the parallax enhancingunit 106.

The SR unit 204 has basically the same configuration as the SR unit 104,and performs the same processing. The SR unit 204 has a disparitydetecting unit 211 which detects disparity between all viewpoints. Thatis to say, the disparity detecting unit 211 is a processing unit thesame as the disparity detecting unit 151 in FIG. 20. In other words, TheSR unit 204 basically has the same configuration as the SR unit 104 inFIG. 20, and the disparity detected by the disparity detecting unit 211is used in super-resolution processing as to the viewpoint images fromthe SR unit 204. Note however, that in the case of the image processingdevice 200, the disparity detected at the disparity detecting unit 211is further supplied to the parallax enhancing unit 206.

The parallax enhancing unit 206 is a processing unit basically the sameas the parallax enhancing unit 106, has the same configuration, andperforms the same processing. Note however, that the parallax enhancingunit 206 performs parallax enhancement processing using disparitybetween all viewpoints supplied from the disparity detecting unit 211.That is to say, while the parallax enhancing unit 206 has theconfiguration of the example illustrated in FIG. 15, for example, thedisparity detecting unit 111 is omitted from the configuration. Thedistance information generating unit 112 identifies disparity betweenviewpoints to be processed out of disparity of all viewpoints suppliedfrom the disparity detecting unit 211, and uses the disparity todetermine the amount of correction for the derivative signals.

Thus, the image processing device 200 can omit disparity detectionprocessing in the parallax enhancement processing, by commonalizing thedisparity detection processing. That is to say, the image processingdevice 200 can reduce the load of parallax enhancement processing. Inother words, the image processing device 200 can reduce the load ofimage processing. Accordingly, the image processing device 200 canperform image processing using hardware resources more efficiently,thereby suppressing increase in costs, larger circuit scale, increasedprocessing time, and so forth.

Flow of Image Processing

An example of the image processing in this case will be described withreference to the flowchart in FIG. 27. The processing of steps S201through S203 are executed in the same way as the processing of stepsS101 through S103.

In step S204, the SR unit 204 uses the disparity detecting unit 211 todetect disparity between all viewpoints, and performs super-resolutionprocessing using the disparity.

The processing of step S205 is executed in the same way as theprocessing of step S105.

In step S206, the parallax enhancing unit 206 enhances parallax betweenthe viewpoint R image and viewpoint L image, using the disparitydetected at the time of the super-resolution processing in step S204.

Upon the processing of step S206 ending, the parallax enhancing unit 206ends the image processing. By performing such processing, the imageprocessing device 200 can omit the disparity detection processing in theparallax enhancement processing, and can alleviate the load of parallaxenhancement processing.

Image Processing Device

While description has been made above where disparity detectionprocessing is commonalized, other processing may be commonalized aswell, such as motion detection processing performed in the noisereduction processing, for example. As described above, the noisereduction processing is performed on each of the viewpoint images. Thatis to say, in the case of processing a multi-viewpoint image, the noisereduction processing is repeated several times. In this case, the motiondetection performed in the noise reduction processing is performedmultiple times, which is redundant. Accordingly, this motion detectioncan be made processing commonalized among the viewpoints, therebysuppressing increase of load.

FIG. 28 is a block diagram illustrating a primary configuration exampleof the image processing device in this case. The image processing device250 in FIG. 28 has basically the same configuration as the imageprocessing device 100, but has a NR unit 255-1 instead of the NR unit105-1, and a NR unit 255-2 instead of the NR unit 105-2. The imageprocessing device 250 also has a motion detecting unit 251. Note that inthe following description, in the event that the NR unit 255-1 and NRunit 255-2 do not have to be distinguished from each other, these willbe simply referred to as “NR unit 255”.

The motion detecting unit 251 performs motion detection between theimage of the super-resolution processing result of the viewpoint R(image of which the resolution has been raised to high resolution), andthe image of the noise reduction processing result of the viewpoint R bythe NR unit 255-1 (image of which the noise has been reduced), andgenerates a motion vector thereof. This motion vector is supplied to theNR unit 255-1 and NR unit 255-2.

The NR unit 255-1 performs noise reduction processing as to theviewpoint R image. The NR unit 255-1 has basically the sameconfiguration as the NR unit 55 (FIG. 9), but the motion predictionprocessing unit 92 is omitted. Instead, the NR unit 255-1 is suppliedwith the output of the motion detecting unit 251 (i.e., the motionvector). The NR unit 255-1 performs motion compensation of the referenceimage using this motion vector, and performs addition determination andaddition processing using the motion compensation results.

The NR unit 255-2 performs noise reduction processing as to theviewpoint L image. The NR unit 255-2 is also supplied with the output ofthe motion detecting unit 251 (i.e., the motion vector). That is to say,the NR unit 255-2 also performs motion compensation of the referenceimage using this motion vector, and performs addition determination andaddition processing using the motion compensation results.

Generally, motion detection processing is a great load, so commonalizingthe motion detection processing in noise reduction processing on theviewpoint images allows the image processing device 250 to reduce theload of noise reduction processing. In other words, the image processingdevice 250 can reduce the load of image processing. Accordingly, theimage processing device 250 can perform image processing using hardwareresources more efficiently, thereby suppressing increase in costs,larger circuit scale, increased processing time, and so forth.

Note that while description has been made above that the motiondetecting unit 251 performs motion detection using the viewpoint Rimage, the viewpoint of the image to be used for motion detection isoptional. For example, the motion detecting unit 251 may perform motiondetection using the viewpoint L image.

Flow of Image Processing

An example of the image processing in this case will be described withreference to the flowchart in FIG. 29. The processing of steps S251through S254 are executed in the same way as the processing of stepsS101 through S104 in FIG. 18.

In step S255, the motion detecting unit 251 performs motion detectionusing the images before and after noise reduction processing for acertain viewpoint.

in step S256, the NR unit 255 performs noise reduction processing oneach viewpoint image to be output, using the motion vector detected bythe processing in step S254.

In step S257, the parallax enhancing unit 106 performs parallaxenhancement to control the amount of parallax between the viewpoints tobe output (between the viewpoint R and viewpoint L).

Upon the processing of step S257 ending, the parallax enhancing unit 106ends the image processing. By performing such processing, with the imageprocessing device 250 the motion detection processing can becommonalized between the viewpoints in the motion detection processing,and the load of noise reduction processing can be reduced.

Image Processing Device

While description has been made above where super-resolution processingand noise reduction processing are performed, but these may beintegrated, with the super-resolution processing and noise reductionprocessing being performed together.

FIG. 30 is a block diagram illustrating a primary configuration exampleof the image processing device in this case. The image processing device300 illustrated in FIG. 30 has basically the same configuration as theimage processing device 200, and performs the same processing, but hasan SR-NR unit 304 instead of the SR unit 204 and NR unit 105.

The SR-NR unit 304 performs both super-resolution processing and noisereduction processing. At this time, the SR-NR unit 304 performssuper-resolution processing and noise reduction processing together. TheSR-NR unit 304 performs such processing on each viewpoint image to beoutput (each of the viewpoint R image and viewpoint L image). Also, theSR-NR unit 304 has a disparity detecting unit 311.

The disparity detecting unit 311 is a processing unit the same as thedisparity detecting unit 211, and detects disparity between theviewpoint images to be output (between the viewpoint R image andviewpoint L image). The SR-NR unit 304 performs super-resolutionprocessing using the detected disparity. Also, the disparity detected bythe disparity detecting unit 311 is supplied to the parallax enhancingunit 206 and used for parallax enhancement processing.

SR-NR Unit

FIG. 31 is a block diagram illustrating a primary configuration exampleof the SR-NR unit. As shown in FIG. 31, the SR-NR unit 304 includes adisparity detecting unit 311, an input image buffer 321, an SR unit 322,an SR processing image buffer 323, an NR unit 324, an NRSR-processedimage buffer 325, and an initial image buffer 326.

The input image buffer 321 obtains images of each viewpoint, suppliedfrom the correcting unit 103, and stores these. The input image buffer321 selects one viewpoint as the viewpoint of interest, either at apredetermined timing or under request from another, and supplies thestored image of the viewpoint of interest to the SR unit 322. Also, theinput image buffer 321 supplies all viewpoint images to the disparitydetecting unit 311.

The disparity detecting unit 311 detects disparity between allviewpoints using the images of all viewpoints read out from the inputimage buffer 321. The disparity detecting unit 311 supplies the detecteddisparity to the SR unit 322. As described above, the disparitydetecting unit 311 supplies the detected disparity to the parallaxenhancing unit 206 as well.

The SR unit 322 performs super-resolution processing using the inputimage supplied from the input image buffer 321, the initial imagesupplied from the initial image buffer 326, and the disparity suppliedfrom the disparity detecting unit 311. The SR unit 322 supplies anSR-processed image, which is the super-resolution processing result, tothe SR-processed image buffer 323, so as to be stored.

The SR-processed image buffer 323 stores the SR-processed image suppliedthereto. Also, the SR-processed image buffer 323 supplies theSR-processed image stored therein to the NR unit 324, either at apredetermined timing or under request from another.

The NR unit 324 performs noise reduction processing using theSR-processed image supplied from the SR-processed image buffer 323 andthe initial image supplied from the initial image buffer 326. The NRunit 324 supplies the processing result (NRSR-processed image) to theNRSR-processed image buffer 325 so as to be stored.

The NRSR-processed image buffer 325 stores the NRSR-processed imagesupplied thereto. Also, the NRSR-processed image buffer 325 outputs theNRSR-processed image stored therein, either at a predetermined timing orunder request from another. Further, the NRSR-processed image buffer 325supplies the NRSR-processed image stored therein to the initial imagebuffer 326, as an initial image, either at a predetermined timing orunder request from another.

The initial image buffer 326 stores the input image supplied from theinput image buffer 321 or the NRSR-processed image supplied from theNRSR-processed image buffer 325. Also, the initial image buffer 326supplies the initial image stored therein to the SR unit 322 and NR unit324, either at a predetermined timing or under request from another.

For example, the super-resolution processing performed by the SR unit104 can be expressed as with the following Expression (2).

f _(SR) =f _(init) −β·W ^(T) H ^(T) D ^(T)·(DHWf _(init) −g _(k))  (2)

Also, for example, the noise reduction processing by the NR unit 105 canbe expressed as with the following Expression (3).

$\begin{matrix}{f_{{NR} \cdot {SR}} = \frac{{\sigma_{SR} \cdot f_{init}} + {\sigma_{init} \cdot f_{SR}}}{\sigma_{init} + \sigma_{SR}}} & (3)\end{matrix}$

On the other hand, the SR-NR unit 304 performs super-resolutionprocessing and noise reduction processing together. That is to say, theprocessing performed by the SR-NR unit 304 can be expressed as with thefollowing Expression (4), in which the above Expressions (2) and (3) arecombined.

$\begin{matrix}{f_{{NR} \cdot {SR}} = {{\left( {1 - {\frac{\beta \cdot \sigma_{init}}{\sigma_{init} + \sigma_{SR}}W^{T}H^{T}D^{T}{DHW}}} \right) \cdot f_{init}} + {\frac{\beta \cdot \sigma_{init}}{\sigma_{init} + \sigma_{SR}}W^{T}H^{T}{D^{T} \cdot {g_{k}.}}}}} & (4)\end{matrix}$

Accordingly, the SR-NR unit 304 can obtain the processing results ofhaving performed both super-resolution processing and noise reductionprocessing, more easily than a case of performing the super-resolutionprocessing and noise reduction processing separately.

The SR-NR unit 304 performs processing such as described above on eachviewpoint to be output (viewpoint R and viewpoint L). Note that theSR-NR unit 304 may be arranged to have multiple configurations such asshown in FIG. 31, so that processing on each image of the multipleviewpoints can be executed in parallel.

Flow of Image Processing

An example of the image processing executed by the image processingdevice 300 will be described with reference to the flowchart in FIG. 32.The processing of steps S301 through S303 are executed in the same wayas the processing of steps S201 through S203 in FIG. 27.

In step S304, the SR-NR unit 304 detects disparity between allviewpoints, and performs super-resolution processing and noise reductionprocessing.

The processing of step S305 is performed the same as with the processingof step S206 in FIG. 27. Upon the processing of step S305 ending, theparallax enhancing unit 206 ends the image processing.

Flow of SRNR Processing

Next, an example of the flow of SRNR processing executed at step S304 inFIG. 32 will be described. Upon the SRNR processing being started, instep S321, the input image buffer 321 stores images of all viewpoints.

In step S322, the disparity detecting unit 311 detects disparity betweenall viewpoints, using the images of all viewpoints stored in step S311.

In step S323, the SR unit 322 selects a viewpoint of interest to beprocessed.

In step S324, the initial image buffer 326 takes the image of theviewpoint of interest as the initial image.

In step S325, the SR unit 322 selects any one viewpoint, and obtains theimage of that viewpoint from the input image buffer 321.

In step S326, the SR unit 322 performs super-resolution processing ofthe viewpoint of interest, using the disparity detected by theprocessing in step S322, the initial image, and the image of theviewpoint selected in step S325.

In step S327, the SR-processed image buffer 323 stores the SR-processedimage of the viewpoint of interest generated by the processing in stepS326.

In step S328, the NR unit 324 reads out the SR-processed image of theviewpoint of interest stored in step S327 from the SR-processed imagebuffer 323, and performs noise reduction processing using theSR-processed image of that viewpoint of interest and the initial imageread out from the initial image buffer 326.

In step S329, the NRSR-processed image buffer 325 stores theNRSR-processed image generated by the processing in step S328.

In step S330, the NRSR-processed image buffer 325 determines whether ornot the super-resolution processing of the viewpoint of interest hasended, and in the event that determination is made that this has notended, the flow advances to step S331.

In step S331, the initial image buffer 326 reads out the NRSR-processedimage stored by the processing in step S329, and stores this as theinitial image. Upon the processing in step S331 ending, the initialimage buffer 326 returns the flow to step S325, and the subsequentprocessing is repeated. In step S325, one viewpoint is newly selectedfrom unprocessed viewpoints.

Upon repeating each processing of steps S325 through S331, anddetermination having been made in step S330 that the super-resolutionprocessing of the viewpoint of interest has ended, the NRSR-processedimage buffer 325 advances the flow to step S332.

In step S332, the NRSR-processed image buffer 325 determines whether ornot all NRSR-processed images of the desired viewpoints (all viewpointsto be output) have been processed. In the event that determination ismade that there remains an unprocessed image, the NRSR-processed imagebuffer 325 returns the flow to step S323, and the subsequent processingis repeated. In step S323, one viewpoint is newly selected fromunprocessed viewpoints.

Upon repeating each processing of steps S323 through S332, anddetermination having been made in step S332 that all NRSR-processedimages of the desired viewpoints (all viewpoints to be output) have beenprocessed, the NRSR-processed image buffer 325 advances the flow to stepS333, and the NRSR-processed image is output. Upon the processing ofstep S333 ending, the NRSR-processed image buffer 325 ends SRNRprocessing, and the flow returns to FIG. 32.

By performing processing as described above, the SR-NR unit 304 canobtain the image of the processing results of having performed bothsuper-resolution processing and noise reduction processing, more easilythan a case of performing the super-resolution processing and noisereduction processing separately. That is to say, the image processingdevice 300 can reduce the load of image processing.

Image Processing Device

While description has been made above regarding an arrangement where theimage processing device 300 uses images other viewpoints of the frame ofinterest, the arrangement is not restricted to this, and an image of theprevious frame may be used as the initial image, for example.

FIG. 34 is a block diagram illustrating a primary configuration exampleof the image processing device. The image processing device 400 shown inFIG. 34 is a device which is basically the same as the image processingdevice 300, has the same configuration, and performs the sameprocessing. Note however, that the image processing device 400 has anSR-NR unit 404, motion detecting unit 411, and initial image generatingunit 412-1 and initial image generating unit 412-2, instead of the SR-NRunit 304. Note that in the following description, in the event that theinitial image generating unit 412-1 and initial image generating unit412-2 do not have to be distinguished from each other, these will besimply referred to as “initial image generating unit 412”.

With the image processing device 400 shown in FIG. 34, the SR-NR unit404 performs super-resolution processing and noise reduction processingusing an initial image generated using the image of the frame ofinterest and the image of a past frame temporally prior to the frame ofinterest. That is to say, with the SR-NR unit 404, the initial imagebuffer 326 in the configuration example described with reference to FIG.31 is omitted, and instead a configuration is made wherein the initialimage supplied from the initial image generating unit 412 is supplied tothe SR unit 322 and NR unit 324, for example.

The motion detecting unit 411 detects motion between frames (motionvector) with regard to the image of the frame of interest and the imageof the past frame, used for generating the initial image. The motiondetecting unit 411 supplies the detected motion vector to the initialimage generating unit 412-1 and initial image generating unit 412-2.

The initial image generating unit 412-1 generates an initial imageregarding one viewpoint to be output (e.g., the viewpoint R). Theinitial image generating unit 412-1 performs motion compensation for theviewpoint R image of the past frame, using the motion vector suppliedfrom the motion detecting unit 411, and generates an initial image usingthis motion compensation result and the viewpoint R image of the frameof interest. That is to say, the initial image generating unit 412-1positions the image of the past frame with the image of the frame ofinterest, and thereafter generates an initial image using both images.

The initial image generating unit 412-2 generates an initial imageregarding one viewpoint to be output (e.g., the viewpoint L). Theinitial image generating unit 412-2 performs motion compensation for theviewpoint L image of the past frame, using the motion vector suppliedfrom the motion detecting unit 411, and generates an initial image usingthis motion compensation result and the viewpoint L image of the frameof interest. That is to say, the initial image generating unit 412-2positions the image of the past frame with the image of the frame ofinterest, and thereafter generates an initial image using both images.

That is to say, the initial image generating unit 412 generates initialimages for each viewpoint to be output. At this time, the initial imagegenerating unit 412 positions the image of the past frame with the imageof the frame of interest, and thereafter generates an initial imageusing both images. The initial image generating unit 412 supplies thegenerated initial images to the SR-NR unit 404. By generating theinitial image used for super-resolution processing and noise reductionprocessing, using an image of a past frame, the image processing device400 can improve the image quality of the generated NRSR image.

Flow of Image Processing

An example of the image processing in this case will be described withreference to the flowchart in FIG. 35. The processing of steps S401through S403 are executed in the same way as the processing of stepsS301 through S303 in FIG. 32.

In step S404, the motion detecting unit 411 performs motion detectingusing the input image and the NRSR-processed image.

In step S405, the initial image generating unit 412 performs motioncompensation of the NRSR-processed image, using the motion vectordetected by the processing in step S404, and generates an initial image.

In step S406, the SR-NR unit 404 detects disparity between allviewpoints, and performs super-resolution processing and noise reductionprocessing using the initial image generated by the processing in stepS405.

In step S407, the parallax enhancing unit 206 performs parallaxenhancement using the disparity detected at the time of thesuper-resolution processing in step S406.

Upon the processing of step S407 ending, the parallax enhancing unit 206ends the image processing. By performing the above processing, the imageprocessing device 400 can improve the image quality of the generatedNRSR image.

Image Processing Device

Further, the arrangement is not restricted to using an image of aviewpoint of interest as the image of the past frame, and an initialimage may be generated using an image of another viewpoint, as well.

FIG. 36 is a block diagram illustrating a primary configuration exampleof an image processing device. The image processing device 450 shown inFIG. 36 is a device basically the same as the image processing device400, has the same configuration, and performs the same processing. Notehowever, that the image processing device 450 further includes, inaddition to the configuration of the image processing device 400, aframe buffer 451-1 and frame buffer 451-2. Also, instead of the initialimage generating unit 412-1 and initial image generating unit 412-2, theimage processing device 450 includes an initial image generating unit452-1 and initial image generating unit 452-2.

Note that in the following description, in the event that the framebuffer 451-1 and frame buffer 451-2 do not have to be distinguished fromeach other, these will be simply referred to as “frame buffer 451”.Also, in the event that the initial image generating unit 452-1 andinitial image generating unit 452-2 do not have to be distinguished fromeach other, these will be simply referred to as “initial imagegenerating unit 452”.

The NRSR-processed image of the viewpoint R output from the SR-NR unit404 is supplied to the frame buffer 451-1 and stored. At a predeterminedtiming or under control of another, the frame buffer 451-1 supplies theNRSR-processed image of the viewpoint R stored in itself to the motiondetecting unit 411 and initial image generating unit 452-1, as an imageof a past frame. The frame buffer 451-1 further supplies theNRSR-processed image of the viewpoint R to the initial image generatingunit 452-2 corresponding to the viewpoint L, as well.

In the same way, the NRSR-processed image of the viewpoint L output fromthe SR-NR unit 404 is supplied to the frame buffer 451-2 and stored. Ata predetermined timing or under control of another, the frame buffer451-2 supplies the NRSR-processed image of the viewpoint L stored initself to the motion detecting unit 411 and initial image generatingunit 452-2, as an image of a past frame. The frame buffer 451-2 furthersupplies the NRSR-processed image of the viewpoint L to the initialimage generating unit 452-1 corresponding to the viewpoint R, as well.

The initial image generating unit 452-1 generates an initial image forthe viewpoint R, using the viewpoint R image of the frame of interest,the viewpoint R image and viewpoint L image of the past frame, and themotion vector detected by the disparity detecting unit 411. The initialimage generating unit 452-2 generates an initial image for the viewpointL, using the viewpoint L image of the frame of interest, the viewpoint Rimage and viewpoint L image of the past frame, and the motion vectordetected by the disparity detecting unit 411.

That is to say, the initial image generating unit 452 generates aninitial image of the viewpoint of interest, using an image of theviewpoint of interest of the frame of interest, an image of theviewpoint of interest of a past frame and an image of a viewpoint otherthan the viewpoint of interest, and the motion vector detected by thedisparity detecting unit 411. Thus, the initial image generating unit452 can generate an initial image with higher image quality.Accordingly, the SR-NR unit 404 can obtain an NRSR image with higherimage quality. That is to say, the image processing device 450 canfurther improve the image quality of the generated NRSR image.

Flow of Image Processing

An example of the flow of image processing executed by the imageprocessing device 450 will be described with reference to the flowchartin FIG. 37. The processing of steps S451 through S454 are executed inthe same way as the processing of steps S401 through S404 in FIG. 35.

In step S455, the initial image generating unit 452 generates an initialimage using the motion vector detected by the processing in step S454,the image of the frame of interest at the viewpoint of interest, theimage of a past frame at the viewpoint of interest, and the image of apast frame at another viewpoint.

The processing of steps S456 and S457 are executed in the same way asthe processing of steps S406 and S407 in FIG. 35. By executingprocessing thus, the image processing device 450 can improve the imagequality of the generated NRSR image.

Image Processing Device

While description has been made above regarding an arrangement where theSR unit and NR unit generate NRSR-processed images for multipleviewpoints, an arrangement may be made where the SR unit and NR unitgenerate an NRSR-processed image for a single viewpoint (i.e., a middleviewpoint), and then generate multiple-viewpoint (e.g., viewpoint R andviewpoint L) images from the single-viewpoint image.

FIG. 38 is a block diagram illustrating a primary configuration exampleof the image processing device. The image processing device 500 isbasically the same device as the image processing device 100 in FIG. 12,has the same configuration, and performs the same processing. Notehowever, that the image processing device 500 has an SR unit 504 insteadof the SR unit 104, an NR unit 505 instead of the NR unit 105, andfurther includes a viewpoint R image generating unit 506-1 and aviewpoint L image generating unit 506-2. Note that in the followingdescription, in the event that the viewpoint R image generating unit506-1 and viewpoint L image generating unit 506-2 do not have to bedistinguished from each other, these will be simply referred to as“image generating unit 506”.

The SR unit 504 does not have multiple super-resolution processing units152 as with the SR unit 104 shown in FIG. 20, and has one disparitydetecting unit 151 and one super-resolution processing unit 152. The onesuper-resolution processing unit 152 performs super-resolutionprocessing on the image of one predetermined viewpoint (e.g., the middleviewpoint). Accordingly, the SR unit 504 outputs the super-resolutionprocessing result image (SR-processed image) of the one predeterminedviewpoint (e.g., the middle viewpoint), and supplies this to the NR unit505.

The SR unit 504 has a depth map generating unit (not illustrated) whichgenerates a depth map which is map information indicating the depthwiseposition of each pixel, using the disparity detected by the disparitydetecting unit 151. This depth map is practically equivalent todisparity, with only the way of expression being different. The SR unit504 supplies this depth map to the viewpoint R image generating unit506-1 and viewpoint L image generating unit 506-2.

The NR unit 505 is a processing unit the same as the NR unit 105, exceptfor processing single-viewpoint images. That is to say, the NR unit 505has a configuration such as shown in FIG. 9, performs noise reductionprocessing on an SR-processed image of one predetermined viewpoint(e.g., the middle viewpoint) supplied from the SR unit 504, generates anNRSR-processed image of that one certain viewpoint (e.g., the middleviewpoint), and supplies this to each of the viewpoint R imagegenerating unit 506-1 and viewpoint L image generating unit 506-2.

The viewpoint R image generating unit 506-1 uses the supplied depth mapto generate a viewpoint R image from the supplied NRSR-processed image,and supplies the generated viewpoint R image to the parallax enhancingunit 106. The viewpoint L image generating unit 506-2 uses the supplieddepth map to generate a viewpoint L image from the suppliedNRSR-processed image, and supplies the generated viewpoint L image tothe parallax enhancing unit 106.

That is to say, the image generating unit 506 uses the depth mapsupplied from the SR unit 504 to generate images of multiple viewpointsfrom the NRSR-processed image supplied form the NR unit 505. Theparallax enhancing unit 106 controls the amount of parallax of themultiple viewpoint images generated in this way.

Thus, super-resolution processing and noise reduction processing, whichis a great load, only has to be performed for a single-viewpoint image,so the image processing device 500 can reduce the load of imageprocessing. That is to say, the image processing device 500 can performimage processing using hardware resources more efficiently, therebysuppressing increase in costs, larger circuit scale, increasedprocessing time, and so forth.

Flow of Image Processing

An example of the flow of image processing in this case will bedescribed with reference to the flowchart in FIG. 39. The following is adescription regarding an example of a case of the SR unit 504 and the NRunit 505 performing processing regarding the middle viewpoint.

The processing of steps S501 through S503 are executed in the same wayas the processing of steps S101 through S103 in FIG. 18.

In step S504, the SR unit 504 performs super-resolution processingregarding, of the multi-viewpoint image, the image of the middleviewpoint, and generates an SR-processed image of the middle viewpoint.Also, the SR unit 504 generates a depth map regarding thismulti-viewpoint image.

In step S505, the NR unit 505 performs noise reduction processing as tothe SR-processed image of the middle viewpoint generated by theprocessing in step S504.

In step S506, the image generating unit 506 uses the depth map generatedby the processing in step S504 to generate images for each viewpoint tobe output, from the NRSR-processed image of the middle viewpoint,generated by the processing in step S505.

In step S507, the parallax enhancing unit 106 enhances (controls) theparallax between the images of each viewpoint, generated by theprocessing in step S506.

Upon the processing in step S507 ending, the parallax enhancing unit 106ends the image processing. By performing this processing, the imageprocessing device 500 can reduce the load of image processing.

Image Processing Device

Further, the initial image may be generated using an image of a pastframe, in the same way as with the case in FIG. 34. FIG. 40 is a blockdiagram illustrating a primary configuration example of an imageprocessing device. The image processing device 550 shown in FIG. 40 is adevice of basically the same configuration as the image processingdevice 500 in FIG. 38, has the same configuration, and performs the sameprocessing. Note however, that in addition to the configuration of theimage processing device 500, the image processing device 550 further hasan initial image generating unit 552.

The initial image generating unit 552 generates an initial image forsuper-resolution processing, using the image of the frame of interestand an image of a past frame, in the same way as with the initial imagegenerating unit 412 in FIG. 34. That is to say, the initial imagegenerating unit 552 supplies the generated initial image to the SR unit504. The SR unit 504 performs super-resolution processing using thisinitial image.

Thus, the image processing device 550 can improve the image quality ofthe SR-processed image. That is to say, the image processing device 550can further improve the image quality of the multi-viewpoint image to beoutput.

Flow of Image Processing

An example of the flow of image processing executed by the imageprocessing device 550 will be described with reference to the flowchartin FIG. 41. The processing of steps S551 through S553 are executed inthe same way as the processing of steps S501 through S503 in FIG. 39.

In step S554, the initial image generating unit 552 generates an initialimage using the image of the frame of interest and an image of a pastframe.

In step S555, the SR unit 504 performs super-resolution processing usingthe initial image generated by the processing in step S554, andgenerates an SR image of the middle viewpoint.

The processing of steps S556 through S558 are executed in the same wayas the processing of steps S505 through S507 in FIG. 39.

Upon the processing of step S558 ending, the parallax enhancing unit 106ends the image processing. By performing such processing, the imageprocessing device 550 can improve the image quality of themulti-viewpoint image to be output.

Image Processing Device

Also, while description has been made above that in the case ofperforming image processing on a multi-viewpoint image, processing isperformed in parallel on each viewpoint image of the multi-viewpointimage, and arrangement may be made where image processing as to theviewpoint images is performed sequentially.

FIG. 42 is a block diagram illustrating a primary configuration exampleof an image processing device. The image processing device 600illustrated in FIG. 42 performs developing, correction, super-resolutionprocessing, and noise reduction processing, on each viewpoint image of amulti-viewpoint image, sequentially for each viewpoint.

To this end, the image processing device 600 includes one each of theviewpoint separating unit 101, developing unit 102, correcting unit 103,SR unit 104, NR unit 105, and parallax enhancing unit 106. The imageprocessing device 600 further has a buffer 601, a viewpoint sequentialreadout unit 602, and a buffer 603, to sequentially process eachviewpoint image.

The viewpoint separating unit 101 separates the multi-viewpoint imageinto individual viewpoints, and supplies to the buffer 601 so as to bestored. The buffer 601 stores the images of the viewpoints supplied fromthe viewpoint separating unit 101.

The viewpoint sequential readout unit 602 sequentially selects oneviewpoint of interest at a time to be processed, from the multipleviewpoints, and reads out the image of that viewpoint of interest fromthe buffer 601. The viewpoint sequential readout unit 602 supplies theimage of the viewpoint of interest that has been read out, to thedeveloping unit 102.

The developing unit 102 develops the image of the viewpoint of interestsupplied thereto, and supplies this to the correcting unit 103. Thecorrecting unit 103 corrects the image of the viewpoint of interestsupplied thereto, and supplies this to the SR unit 104. The SR unit 104performs super-resolution processing on the image of the viewpoint ofinterest supplied thereto so as to raise the resolution thereof to highresolution, and supplies this to the NR unit 105. The NR unit 105subjects the high-resolution image of the viewpoint of interest suppliedthereto to noise reduction processing, and supplies the image of theviewpoint of interest with reduced noise to the buffer 603 so as to bestored.

Upon the above processing on the image of the viewpoint of interestends, the viewpoint sequential readout unit 602 switches the viewpointof interest to the next viewpoint, reads out the image of the newviewpoint of interest from the buffer 601, and supplies this to thedeveloping unit 102. That is to say, the developing unit 102 through NRunit 105 each perform their respective processing on the image of thenew viewpoint of interest. Repeating such processing subjects allviewpoints of the multi-viewpoint image to processing, and storage inthe buffer 603.

Upon storing the images of all viewpoints, the buffer 603 supplies theimages of all viewpoints to the parallax enhancing unit 106. Theparallax enhancing unit 106 controls the parallax (stereoscopic effect)of the multi-viewpoint image supplied thereto. The parallax enhancingunit 106 then outputs the multi-viewpoint image with the amount ofparallax thereof having been controlled. Note that in this case, the SRunit 104 performs processing one viewpoint at a time, so initial imagesare preferably generated by making reference to images of past frames.

Due to such image processing, the images of the frames (0, 1, 2 . . . )of each frame of the input multi-viewpoint image (L, R) are arrayed inone row (L0, R0, L1, R1, L2, R2, . . . ) as shown to the bottom in FIG.42, and the processing of developing through noise reduction isperformed one image at a time. The images following processing arestored in the buffer 603 and thereafter output in increments of framesas multi-viewpoint images (L, R).

Thus, the image processing device 600 can process multi-viewpoint imageswith a smaller configuration. That is to say, the image processingdevice 600 can perform image processing using hardware resources moreefficiently, thereby suppressing increase in costs, larger circuitscale, increased processing time, and so forth.

Flow of Image Processing

An example of the flow of image processing executed by the imageprocessing device 600 will be described with reference to the flowchartin FIG. 43.

In step S601, the viewpoint separating unit 101 separates the inputmulti-viewpoint image into individual viewpoints.

In step S602, the buffer 601 stores the images of the viewpoints.

In step S603, the viewpoint sequential readout unit 602 selects anviewpoint of interest, and reads out the image of that viewpoint ofinterest, that has been stored in step S602, from the buffer 601.

In step S604, the developing unit 102 develops the image of theviewpoint of interest read out by the processing in step S603.

In step S605, the correcting unit 103 corrects the image of theviewpoint of interest developed by the processing in step S604.

In step S606, the SR unit 104 performs super-resolution processing onthe image of the viewpoint of interest corrected by the processing instep S605.

In step S607, the NR unit 105 subjects the image of the viewpoint ofinterest, of which the resolution has been raised to high-resolution bythe processing in step S606 (SR-processed image), to noise reductionprocessing.

In step S608, the buffer 603 stores the image of the viewpoint ofinterest of which noise has been reduced by the processing in step S607.

In step S609, the viewpoint sequential readout unit 602 determineswhether or not images of all viewpoints have been processed, and in theevent that determination is made that there is an unprocessed viewpointremaining, the flow returns to step S603, a new unprocessed viewpoint isselected as the viewpoint of interest, and subsequent processing isexecuted.

By the processing of steps S603 through S609 being repeatedly executed,the images of all viewpoints to be output are processed.

In the event that determination is made in step S609 that all viewpointimages to be output have been processed, the viewpoint sequentialreadout unit 602 advances the flow to step S610.

In step S610, the parallax enhancing unit 106 performs parallaxenhancement processing using the multiple images with differentviewpoints stored in the buffer 603, and controls the amount ofdisparity between the viewpoints.

Upon the processing of step S610 ends, the parallax enhancing unit 106ends the image processing. By performing processing in this way, theimage processing device 600 can perform image processing using hardwareresources more efficiently, thereby suppressing increase in costs andlarger circuit scale.

2. Second Embodiment Imaging Apparatus

The image processing device described above may be configured as a partof another device. For example, it may be an image processing unit builtinto an imaging apparatus.

FIG. 44 is a block diagram illustrating a primary configuration exampleof an imaging apparatus to which the present technology has beenapplied. As shown in FIG. 44, an imaging apparatus 700 is an imagingapparatus using a technique called light field photography, and is aso-called light field camera. The imaging apparatus 700 includes anoptical unit 711, an image sensor 712, an A/D (Analog-to-Digital)converter 713, an operating unit 714, a control unit 715, an imageprocessing unit 716, a display unit 717, a codec processing unit 718,and a recording unit 719.

The optical unit 711 is made up of a main lens, diaphragm, and so forth,and serves to adjust the focal point to the subject, collect light fromthe position where the focal point meets, and supplies to the imagesensor 712.

The image sensor 712 is any imaging device, such as a CCD (ChargeCoupled Device) image sensor, CMOS (Complementary Metal OxideSemiconductor) image sensor, or the like. The image sensor 712 receiveslight input via the optical unit 711 (incident light), and performsphotoelectric conversion thereof to obtain signals corresponding to theintensity of the light (image signals). A microlens is provided forevery certain plural number of pixels on the light receiving face of theimage sensor 712. that is to say, each of the pixels of the image sensor712 receive incident light input via one of the microlenses.Accordingly, each of the pixels of the image sensor 712 performphotoelectric conversion of light input from a direction correspondingto a position of a microlens. The image sensor 712 supplies imagesignals of each viewpoint image obtained in this way to the A/Dconverter 713 as image signals of a single imaged image.

The A/D converter 713 converts the image signal, supplied from the imagesensor 712 at a predetermined timing, into a digital image signal(hereinafter also referred to as “pixel signal” as appropriate), andsequentially supplies to the image processing unit 716 at predeterminedtiming.

The operating unit 714 is configured of, for example, a jog dial, keys,buttons, a touch panel, or the like, to accept input of user operations,and supply signals corresponding to the operation input to the controlunit 715.

The control unit 715 controls the optical unit 711, image sensor 712,A/D converter 713, image processing unit 716, display unit 717, codecprocessing unit 718, and recording unit 719, based on signalscorresponding to the user operation input that has been input at theoperating unit 714.

The image processing unit 716 subjects the image signals supplied fromthe A/D converter 713 to optional image processing, such as viewpointseparation, super-resolution processing, noise reduction processing,parallax enhancement, white balance adjustment, demosaicing, gammacorrection, and so forth, for example. The image processing unit 716supplies the image signals following processing to the display unit 717and codec processing unit 718.

The display unit 717 is configured of a liquid crystal display or thelike, for example, and displays an imaged image based on image signalsfrom the image processing unit 716.

The codec processing unit 718 subjects the image signals from the imageprocessing unit 716 to encoding according to a predetermined format, andsupplies the image data obtained as a result of the encoding processingto the recording unit 719.

The recording unit 719 records image data from the codec processing unit718. The image data recorded in the recording unit 719 is read out tothe image processing unit 716 as appropriate, and thus decoded andsupplied to the display unit 717, so that a corresponding image isdisplayed.

With the imaging apparatus 700 which obtains imaged images ofmulti-viewpoint images, the technology of the present disclosure can beapplied to the image processing unit 716, so that the configurations(functions) of the examples of the image processing devices describedwith the first embodiment are included therein. By applying the presenttechnology to the image processing unit 716, the image processing unit716 can control the stereoscopic effect of multi-viewpoint images, andstereoscopic effect of a more suitable degree can be obtained, in thesame way as with the case of the first embodiment. That is to say, theimaging apparatus 700 can generate multi-viewpoint image data having amore suitable stereoscopic effect.

It should be noted that the present technology is not restricted to theimaging apparatus of the configuration described above, and can beapplied to imaging apparatuses of any configuration, as long as animaging apparatus which generates multi-viewpoint images as imagedimages.

3. Third Embodiment Personal Computer

The above-described series of processing can be carried out by hardware,or can be carried out by software. In this case, the above-describedseries of processing can be configured as a personal computer such asshown in FIG. 45, for example.

In FIG. 45, a CPU (Central Processing Unit) 801 of a personal computer800 executes various types of processing, following programs stored inROM (Read Only Memory) 802 or programs loaded from a storage unit 813 toRAM (Random Access Memory) 803. The RAM 803 also stores various types ofdata used by the CPU 801 to execute various types of processing, and soforth, as appropriate.

The CPU 801, ROM 802, and RAM 803, are connected one with another by abus 804. An input/output interface 810 is also connected to this bus804.

Also connected to the input/output interface 810 are an input unit 811made up of an input device such as a keyboard or mouse or the like, oran input terminal or the like, an output unit 812 configured of adisplay made up of a CRT (Cathode Ray Tube) or LCD (Liquid CrystalDisplay) or the like, and an output device such as a speaker or the likeor an output terminal or the like, a storage unit 813 configured of astorage medium such as a hard disk or flash memory or the like, acontrol unit and so forth for controlling input/output of the storagemedium, and a communication unit 814 made up of a communication devicesuch as a modem or LAN (Local Area Network) interface or the like. Thecommunication unit 814 performs communication processing with othercommunication devices via a network, including the Internet for example.

Also connected to the input/output interface 810 is a drive 815, asappropriate. Mounted to the drive 815 as appropriate is, removable media821 such as a magnetic disk, optical disc, magneto-optical disc, orsemiconductor memory or the like. The drive 815 reads out computerprograms, data, and so forth, from the removable media 821 mounted toitself, under control of the CPU 801, for example. The data, computerprograms, or the like, that have been read out, are supplied to the RAM803, for example. Also, the computer programs read out from theremovable media 821 are installed to the storage unit 813, asappropriate.

In the event of carrying out the above series of processing by software,a program making up the software is installed from a network orrecording medium.

Note that this recording medium is not restricted to being configured ofthe removable media 821 made up of a magnetic disk (including flexibledisks), optical disc (including CD-ROM (Compact Disc-Read Only Memory)and DVD (Digital Versatile Disc)), magneto optical disc (including MD(Mini Disc)), or semiconductor memory, in which a program is recorded,distributed separately from the device proper so as to distribute theprogram to the user, such as shown in FIG. 45 for example, and may beconfigured of the ROM 802, a hard disk included in the storage unit 813,or the like, distributed to the user in a state of being built into thedevice proper beforehand.

Note that the program executed by the computer may be a program in whichprocessing is performed in a time-series manner following the orderdescribed in the present specification, or may be a program in whichprocessing is performed concurrently, or at a timing as appropriate whena callout is performed or the like.

Also, with the present specification, steps described in the programrecorded in the recording medium include processing which is performedin a time-series manner following the order described therein as amatter of course, and also include processing which is performedconcurrently or in parallel, even if not processed in time-sequence.

Also, with the present specification, the term “system” refers to theentirety of equipment made up of multiple devices or apparatuses.

Also, in the above description, a configuration described as being onedevice (or processing unit) may be divided so as to be configured ofmultiple devices (or processing units). Conversely, a configurationdescribed as being multiple devices (or processing units) may beintegrated so as to be configured of one device (or processing unit).Also, configurations other than the configuration of devices (orprocessing units) may be added thereto, as a matter of course. Further,a portion of the configuration of a certain device (or processing unit)may be included in the configuration of another device (or processingunit), as long as the configuration or operation of the overall systemis substantially the same. That is to say, the present technology is notrestricted to the above-described embodiments, and various modificationsmay be made without departing from the essence of the presenttechnology.

Note that the present technology can assume configurations such as thefollowing.

(1) An image processing device including:

-   -   a viewpoint separating unit configured to separate        multi-viewpoint image data, including images of multiple        viewpoints and representing intensity distribution of light and        the direction of travel of light according to positions and        pixel values of pixels, into a plurality of single-viewpoint        image data for each of the individual viewpoints; and

a parallax control unit configured to control amount of parallax betweenthe plurality of single-viewpoint image data obtained by separation intoindividual viewpoints by the viewpoint separating unit.

(2) The image processing device according to (1), wherein the parallaxcontrol unit controls the amount of parallax by adding or subtracting aderivative signal, obtained by performing derivation of thesingle-viewpoint image data, to or from each single-viewpoint imagedata.

(3) The image processing device according to (2), wherein the parallaxcontrol unit

detects disparity, which indicates amount of parallax of an object to becontrolled, and

based on the detected disparity, corrects the derivative signal andcontrols the amount of parallax by adding or subtracting the derivativesignal following correction to or from each single-viewpoint image data.

(4) The image processing device according to any one of (1) through (3),further including:

a super-resolution processing unit configured to performsuper-resolution processing where image resolution is raised to highresolution for each of the plurality of single-viewpoint image dataobtained by separation into individual viewpoints by the viewpointseparating unit;

wherein the parallax control unit controls amount of parallax betweenthe plurality of single-viewpoint image data of which the resolution hasbeen raised to high resolution by the super-resolution processing unit.

(5) The image processing device according to (4), wherein thesuper-resolution processing unit detects disparity which indicatesamount of parallax, between each viewpoint, and performssuper-resolution processing of the single-viewpoint image data, usingthe detected disparity.

(6) The image processing device according to (5), wherein the parallaxcontrol unit controls the amount of parallax using the disparitydetected by the super-resolution processing unit.

(7) The image processing device according to any one of (4) through (6),further including:

a noise reduction processing unit configured to perform noise reductionprocessing to reduce noise on each of the plurality of single-viewpointimage data, of which the resolution has been raised to high resolutionby the super-resolution processing unit;

wherein the parallax control unit controls the amount of parallaxbetween the plurality of single-viewpoint image data, of which noise hasbeen reduced by the noise reduction processing unit.

(8) The image processing device according to (7), wherein the noisereduction processing unit

performs motion detection with images before and after processing,

performs motion compensation on an image after processing using thedetected motion vector, and

calculates the arithmetic mean of

-   -   the image following processing that has been subjected to motion        compensation, and    -   the image before processing.

(9) The image processing device according to (8), wherein the noisereduction processing unit uses the detected motion vector to perform thenoise reduction processing on each of the plurality of single-viewpointimage data.

(10) The image processing device according to any one of (1) through(3), further including:

a super-resolution and noise reduction processing unit configured toperform, on each of the plurality of single-viewpoint image dataobtained by separation into individual viewpoints by the viewpointseparating unit,

-   -   super-resolution processing to raise the resolution of images to        high resolution, and    -   noise reduction processing to reduce noise;

wherein the parallax control unit controls the amount of parallaxbetween the plurality of single-viewpoint image data, of whichresolution has been raised to high resolution and noise has been reducedby the super-resolution and noise reduction processing unit.

(11) The image processing device according to (10), wherein thesuper-resolution and noise reduction processing unit

performs the noise reduction processing on each of the super-resolutionprocessing results of the multiple single-viewpoint image data, and

performs the super-resolution processing using the noise reductionprocessing results.

(12) The image processing device according to either (10) or (11),further including:

an initial image generating unit configured to generate an initial imageusing an image of a frame of interest which is to be processed, and animage of a past frame processed prior to the frame of interest;

wherein the super-resolution and noise reduction processing unitperforms the super-resolution processing using the initial imagegenerated by the initial image generating unit.

(13) The image processing device according to (12), wherein the initialimage generating unit

detects motion between the image of the frame of interest and the imageof the past frame,

performs motion compensation of the image of the past frame using thedetected motion vector, and

generates the initial image using the image of the past frame subjectedto motion compensation and the image of the frame of interest.

(14) The image processing device according to (12), wherein the initialimage generating unit generates the initial image using an image of aviewpoint of interest to be processed in the frame of interest, an imageof the viewpoint of interest in the past frame, and an image of anotherviewpoint which is not the viewpoint of interest in the past frame.

(15) The image processing device according to (1), further including:

a super-resolution processing unit configured to performsuper-resolution processing of the single-viewpoint image data to raisethe resolution of an image to high resolution;

a noise reduction processing unit configured to perform noise reductionprocessing to reduce noise of the single-viewpoint image data of whichthe resolution has been raised to high resolution by thesuper-resolution processing unit; and

an image generating unit configured to generate the plurality ofsingle-viewpoint image data, using the single-viewpoint image data ofwhich the noise has been reduced by the noise reduction processing unit;

wherein the parallax control unit controls the amount of parallaxbetween the plurality of single-viewpoint image data generated by theimage generating unit.

(16) The image processing device according to (15), further including:

an initial image generating unit configured to generate an initialimage, using an image of a frame of interest to be processed, and animage of a past frame processed prior to the frame of interest;

wherein the super-resolution processing unit performs thesuper-resolution processing using the initial image generated by theinitial image generating unit.

(17) The image processing device according to (1), further including:

a first storage unit configured to store the plurality ofsingle-viewpoint image data obtained by separation into individualviewpoints by the viewpoint separating unit;

a viewpoint sequential readout unit configured to read out the pluralityof single-viewpoint image data stored in the first storage unit, oneviewpoint at a time;

a super-resolution processing unit configured to performsuper-resolution processing to raise the resolution of thesingle-viewpoint image data read out from the viewpoint sequentialreadout unit to high resolution;

a noise reduction processing unit configured to perform noise reductionprocessing to reduce the noise of the single-viewpoint image data ofwhich the resolution has been raised to high resolution by thesuper-resolution processing unit; and

a second storage unit configured to store the single-viewpoint imagedata of which noise has been reduced by the noise reduction processingunit;

wherein the parallax control unit controls the amount of parallaxbetween the plurality of single-viewpoint image data stored in thesecond storage unit.

(18) An image processing method of an image processing device, themethod including:

a viewpoint separating unit separating multi-viewpoint image data,including images of multiple viewpoints and representing intensitydistribution of light and the direction of travel of light according topositions and pixel values of pixels, into a plurality ofsingle-viewpoint image data for each of the individual viewpoints; and

a parallax control unit controlling amount of parallax between theplurality of single-viewpoint image data obtained by separation intoindividual viewpoints.

(19) A computer-readable recording medium in which is recorded aprogram, to cause a computer to function as:

a viewpoint separating unit configured to separate multi-viewpoint imagedata, including images of multiple viewpoints and representing intensitydistribution of light and the direction of travel of light according topositions and pixel values of pixels, into a plurality ofsingle-viewpoint image data for each of the individual viewpoints; and

a parallax control unit configured to control amount of parallax betweenthe plurality of single-viewpoint image data obtained by separation intoindividual viewpoints by the viewpoint separating unit.

(20) A program to cause a computer to function as:

a viewpoint separating unit configured to separate multi-viewpoint imagedata, including images of multiple viewpoints and representing intensitydistribution of light and the direction of travel of light according topositions and pixel values of pixels, into individual viewpoints; and

a parallax control unit configured to control amount of parallax betweenthe plurality of single-viewpoint image data obtained by separation intoa plurality of single-viewpoint image data for each of the individualviewpoints by the viewpoint separating unit.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

What is claimed is:
 1. An image processing device comprising: aplurality of noise reduction processing units, wherein each noisereduction processing unit is configured to perform noise reductionprocessing to reduce noise on viewpoint image data of a plurality ofviewpoint image data; and a motion detecting unit configured to: detectmotion vector between images before and after the noise reductionprocessing from viewpoint image data of one of the plurality of noisereduction processing units; and supply the detected motion vector toeach of the plurality of noise reduction processing units.
 2. The imageprocessing device according to claim 1, further comprising: asuper-resolution processing unit configured to perform super-resolutionprocessing where image resolution is raised to a high resolution on eachof the plurality of viewpoint image data, wherein each of the pluralityof noise reduction processing units is configured to perform the noisereduction processing to reduce noise on the viewpoint image data of theplurality of viewpoint image data, of which image resolution has beenraised to a high resolution by the super-resolution processing unit. 3.The image processing device according to claim 2, wherein thesuper-resolution processing unit is configured to detect disparity foreach of the plurality of viewpoint image data, and perform thesuper-resolution processing on each of the plurality of viewpoint imagedata using the detected disparity.
 4. The image processing deviceaccording to claim 1, further comprising: a viewpoint separating unitconfigured to separate multi-viewpoint image data, according topositions and pixel values of pixels, into the plurality of viewpointimage data for each of a plurality of individual viewpoints; whereineach of the plurality of noise reduction processing units is configuredto perform the noise reduction processing to reduce noise on theviewpoint image data of the plurality of viewpoint image data obtainedby the separation into the plurality of individual viewpoints by theviewpoint separating unit.
 5. The image processing device according toclaim 1, further comprising: a parallax control unit configured tocontrol an amount of parallax between the plurality of viewpoint imagedata, of which noise has been reduced by the plurality of noisereduction processing units.
 6. The image processing device according toclaim 1, wherein each of the plurality of the noise reduction processingunits is configured to perform motion compensation, on the image afterthe noise reduction processing, using the detected motion vector, andcalculate an arithmetic mean of the image after the noise reductionprocessing that has been subjected to motion compensation, and the imagebefore the noise reduction processing.
 7. An image processing method ofan image processing device, the method comprising: performing noisereduction processing to reduce noise on each viewpoint image data of aplurality of viewpoint image data; detecting motion vector betweenimages before and after the noise reduction processing from viewpointimage data of the plurality of viewpoint image data; and performingmotion compensation for the plurality of viewpoint image data based onthe detected motion vector.
 8. The image processing method according toclaim 7, further comprising supplying the detected motion vector to aplurality of noise reduction units, wherein the plurality of noisereduction units are configured to perform the noise reduction processingto reduce noise on the plurality of viewpoint image data.
 9. The imageprocessing method according to claim 7, further comprising separatingmulti-viewpoint image data according to positions and pixel values ofpixels, into the plurality of viewpoint image data for each of aplurality of individual viewpoints.
 10. The image processing methodaccording to claim 7, further comprising performing super-resolutionprocessing where image resolution is raised to a high resolution foreach of the plurality of viewpoint image data.
 11. The image processingmethod according to claim 10, further comprising detecting disparity foreach of the plurality of viewpoint image data, and performing thesuper-resolution processing on each of the plurality of viewpoint imagedata using the detected disparity.
 12. The image processing methodaccording to claim 10, wherein the noise reduction processing isperformed to reduce noise on each of the plurality of viewpoint imagedata, of which the image resolution has been raised to high resolution.13. The image processing method according to claim 7, further comprisingcontrolling an amount of parallax between the plurality of viewpointimage data, of which noise has been reduced.
 14. The image processingmethod according to claim 7, wherein the motion compensation isperformed on the image after the noise reduction processing using thedetected motion vector.
 15. The image processing method according toclaim 14, further comprising calculating an arithmetic mean of the imageafter the noise reduction processing that has been subjected to motioncompensation, and the image before the noise reduction processing.
 16. Anon-transitory computer-readable medium having stored thereon a set ofcomputer-executable instructions which when executed by a computer causea computer to perform the steps comprising: performing noise reductionprocessing to reduce noise on each viewpoint image data of a pluralityof viewpoint image data; detecting motion vector between images beforeand after the noise reduction processing from viewpoint image data ofthe plurality of viewpoint image data; and performing motioncompensation for the plurality of viewpoint image data based on thedetected motion vector.